Current
Population
Survey
Technical Paper 63RV
Design
and
Methodology
TP63RV
U.S. Department
of Labor
BUREAU OF
LABOR STATISTICS
U.S. Department of
Commerce
Economics and Statistics
Administration
U.S. CENSUS BUREAU
Acknowledgments
This technical paper was written under the supervision of Donna L. Kostanich of the
U.S. Census Bureau and Cathryn S. Dippo of the Bureau of Labor Statistics. It has
been produced through the combined efforts of many individuals. Some wrote sections
of the paper, some reviewed sections of the paper, and others did both. Some are still
with their agencies and some have since left.
Contributing from the U.S. Census Bureau were Adelle Berlinger, Chet Bowie,
John Bushery, Lawrence S. Cahoon, Maryann Chapin, Jeffrey S. Corteville,
Deborah Fenstermaker, Robin Fisher, Richard Griffiths, Carol A. Gunlicks, Frederick W. Hollmann, David V. Hornick, Vicki J. Huggins, Melinda Kraus, J. Mackey
Lewis, Khandaker A. Mansur, Pamela D. McGovern, Thomas F. Moore, John
Paletta, Maria Reed, Brad L. Rigby, Jennifer M. Rothgeb, Robert Rothhaas, Blair
Russell, Wendy Scholetzky, Irwin Schreiner, Harland H. Shoemaker Jr., Katherine
J. Thompson, Ronald R. Tucker, Gregory D. Weyland, Patricia Wilson, Andrew
Zbikowski, and the many other people who have held positions in the Current
Population Survey Branch of the Demographic Statistical Methods Division, as well as
other divisions throughout the Census Bureau.
Contributing from the Bureau of Labor Statistics were John E. Bregger, Martha Duff,
Gloria P. Green, Harvey R. Hamel, Brian Harris-Kojetin, Diane E. Herz, Vernon C.
Irby, Bodhini R. Jayasuriya, Janice K. Lent, Robert J. McIntire, Sandi Mason,
Thomas Nardone, Anne E. Polivka, Ed Robison, Philip L. Rones, Richard Tiller,
Clyde Tucker, and Tamara Sue Zimmerman.
Sharon Berwager, Mary Ann Braham, and Cynthia L. Wellons-Hazer provided
assistance in preparing the many drafts of this paper. John S. Linebarger was the
Census Bureau’s Contracting Officer’s Technical Representative.
The review of the final manuscript prior to publication was coordinated by Antoinette
Lubich of the Census Bureau. Patricia Dean Brick served as an editorial consultant
and Joseph Waksberg of Westat, Inc. assisted in the document’s review for technical
accuracy. Kenton Kilgore and Larry Long of the Census Bureau provided final
comments. EEI Communications constructed the subject index and list of acronyms.
Kim D. Ottenstein, Cynthia G. Brooks, Frances B. Scott, Susan M. Kelly,
Neeland Queen, Patricia Edwards, Gloria T. Davis and Laurene V. Qualls of the
Administrative and Customer Services Division, Walter C. Odom, Chief, provided
publications and printing management, graphics design and composition, and editorial
review for print and electronic media. General direction and production management
were provided by Michael G. Garland, Assistant Division Chief, and Gary J. Lauffer,
Chief, Publications Services Branch.
William Hazard and Alan R. Plisch of the Census Bureau provided assistance
in placing electronic versions of this document on the Internet (see
http://www.census.gov/prod/2002pubs/tp63rv.pdf).
We are grateful for the assistance of these individuals, and all others who are not
specifically mentioned, for their help with the preparation and publication of this
document.
Current
Population
Survey
Technical Paper 63RV
Design
and
Methodology
TP63RV
Issued March 2002
U.S. Department of Labor
Elaine L. Chao,
Secretary
Bureau of Labor Statistics
Lois Orr,
Acting Commissioner
U.S. Department of Commerce
Donald L. Evans,
Secretary
Samuel W. Bodman,
Deputy Secretary
Economics and Statistics Administration
Kathleen B. Cooper,
Under Secretary for
Economic Affairs
U.S. CENSUS BUREAU
William G. Barron, Jr.,
Acting Director
ECONOMICS
AND STATISTICS
ADMINISTRATION
Economics
and Statistics
Administration
Kathleen B. Cooper,
Under Secretary
for Economic Affairs
U.S. CENSUS BUREAU
BUREAU OF LABOR STATISTICS
William G. Barron, Jr.,
Acting Director
Lois Orr, Acting Commissioner
Lois Orr, Deputy Commissioner
William G. Barron, Jr.,
Deputy Director
Cathryn S. Dippo, Associate Commissioner
for Survey Methods Research
John M. Galvin, Associate Commissioner
Office of Employment and Unemployment
Statistics
Philip L. Rones, Assistant Commissioner
Office of Current Employment Analysis
Nancy M. Gordon, Associate Director for
Demographic Programs
Cynthia Z. F. Clark, Associate Director for
Methodology and Standards
Foreword
The Current Population Survey (CPS) is one of the oldest, largest, and most well-recognized
surveys in the United States. It is immensely important, providing information on many of the things
that define us as individuals and as a society—our work, our earnings, our education. It is also
immensely complex. Staff of the Census Bureau and the Bureau of Labor Statistics have
attempted, in this publication, to provide data users with a thorough description of the design and
methodology used in the CPS. The preparation of this technical paper was a major undertaking,
spanning several years and involving dozens of statisticians, economists, and others from the two
agencies.
This paper is the first major update of CPS documentation in more than two decades, and, while
the basic approach to collecting labor force and other data through the CPS has remained intact
over the intervening years, much has changed. In particular, a redesigned CPS was introduced in
January 1994, centered around the survey’s first use of a computerized survey instrument by field
interviewers. The questionnaire itself was rewritten to better communicate CPS concepts to the
respondent, and to take advantage of computerization.
This document describes the design and methodology that existed for the CPS as of December
1995. Some of the appendices cover updates that have been made to the survey since then.
Users of CPS data should have access to up-to-date information about the survey’s methodology. The advent of the Internet allows us to provide updates to the material contained in this report
on a more timely basis. Please visit our CPS web site at http://www.bls.census.gov/cps, where
updated survey information will be made available. Also, we welcome comments from users about
the value of this document and ways that it could be improved.
Kenneth Prewitt
Director
U.S. Census Bureau
March 2000
Katharine G. Abraham
Commissioner
Bureau of Labor Statistics
March 2000
v
Contents
Summary of Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xi
Chapter 1. Background
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1–1
Chapter 2. History of the Current Population Survey
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Major Changes in the Survey: A Chronology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2–1
2–1
2–6
Chapter 3. Design of the Current Population Survey Sample
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Survey Requirements and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
First Stage of the Sample Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Second Stage of the Sample Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Third Stage of the Sample Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rotation of the Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3–1
3–1
3–2
3–7
3–14
3–15
3–17
Chapter 4. Preparation of the Sample
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Listing Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Third Stage of the Sample Design (Subsampling). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviewer Assignments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4–1
4–4
4–6
4–6
Chapter 5. Questionnaire Concepts and Definitions for the Current
Population Survey
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Structure of the Survey Instrument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Concepts and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5–1
5–1
5–1
5–7
Chapter 6. Design of the Current Population Survey Instrument
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Motivation for the Redesign of the Questionnaire Collecting of Labor Force Data . . . . . .
Objectives of the Redesign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Highlights of the Questionnaire Revision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Continuous Testing and Improvements of the Current Population Survey and Its
Supplements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6–1
6–1
6–1
6–3
6–8
6–8
Chapter 7. Conducting the Interviews
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Noninterviews and Household Eligibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Initial Interview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Subsequent Months’ Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7–1
7–1
7–3
7–4
vi
Chapter 8. Transmitting the Interview Results
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Transmission of Interview Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8–1
8–1
Chapter 9. Data Preparation
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Daily Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Industry and Occupation (I&O) Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Edits and Imputations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9–1
9–1
9–1
9–1
Chapter 10. Estimation Procedures for Labor Force Data
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unbiased Estimation Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Adjustment for Nonresponse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ratio Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
First-Stage Ratio Adjustment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Second-Stage Ratio Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Composite Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Seasonal Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monthly State and Substate Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Producing Other Labor Force Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10–1
10–1
10–2
10–3
10–3
10–5
10–9
10–10
10–10
10–12
10–14
Chapter 11. Current Population Survey Supplemental Inquiries
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Criteria for Supplemental Inquiries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Recent Supplemental Inquiries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Annual Demographic Supplement (March Supplement). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11–1
11–1
11–2
11–4
11–8
Chapter 12. Data Products From the Current Population Survey
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Bureau of Labor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
U.S. Census Bureau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12–1
12–1
12–2
Chapter 13. Overview of Data Quality Concepts
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Quality Measures in Statistical Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Quality Measures in Statistical Process Monitoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13–1
13–1
13–2
13–3
13–3
Chapter 14. Estimation of Variance
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Variance Estimates by the Replication Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Method for Estimating Variance for 1990 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Variances for State and Local Area Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generalizing Variances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Variance Estimates to Determine Optimum Survey Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Total Variances As Affected by Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Design Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14–1
14–1
14–2
14–3
14–3
14–5
14–6
14–7
14–9
vii
Chapter 15. Sources and Controls on Nonsampling Error
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sources of Coverage Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Controlling Coverage Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sources of Error Due to Nonresponse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Controlling Error Due to Nonresponse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sources of Response Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Controlling Response Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sources of Miscellaneous Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Controlling Miscellaneous Errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15–1
15–2
15–2
15–4
15–5
15–6
15–6
15–8
15–8
15–9
Chapter 16. Quality Indicators of Nonsampling Errors
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Coverage Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nonresponse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Response Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mode of Interview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Time in Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Proxy Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16–1
16–1
16–3
16–5
16–6
16–7
16–10
16–10
16–10
Figures
3–1
4–1
5–1
7–1
7–2
7–3
7–4
7–5
7–6
7–7
7–8
7–9
7–10
8–1
11–1
16–1
16–2
F–1
J–1
CPS Rotation Chart: January 1996 - April 1998. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example of Systematic Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Questions for Employed and Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Introductory Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Noninterviews: Types A, B, and C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Noninterviews: Main Housing Unit Items Asked for Types A, B, and C. . . . . . . . . . . . . .
Interviews: Main Housing Unit Items Asked in MIS 1 and Replacement
Households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary Table for Determining Who Is To Be Included As a Member of the
Household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviews: Main Demographic Items Asked in MIS 1 and Replacement
Households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Demographic Edits in the CPS Instrument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviews: Main Items (Housing Unit and Demographic) Asked in MIS 5 Cases. . .
Interviewing Results (December 1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Telephone Interview Rates (December 1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Overview of CPS Monthly Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Diagram of the ADS Weighting Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Average Yearly Noninterview and Refusal Rates for the CPS 1964 - 1996 . . . . . . . . .
Monthly Noninterview and Refusal Rates for the CPS 1984 - 1996 . . . . . . . . . . . . . . . . .
2000 Decennial and Survey Boundaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
SCHIP Rotation Chart: September 2000 to December 2002 . . . . . . . . . . . . . . . . . . . . . . . . .
3–16
4–3
5–6
7–2
7–3
7–4
7–5
7–6
7–7
7–8
7–9
7–10
7–10
8–3
11–6
16–2
16–4
F–2
J–5
Illustrations
1
2
3
4
5
Segment Folder, BC-1669(CPS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unit/Permit Listing Sheet (Unit Segment) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unit/Permit Listing Sheet (Multiunit Structure) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unit/Permit Listing Sheet (Large Special Exception) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Incomplete Address Locator Actions, BC-1718(ADP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B–2
B–4
B–5
B–6
B–7
viii
Illustrations—Con.
6
7
8
9
10
11
12
13
Area Segment Listing Sheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Area Segment Map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Segment Locator Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Map Legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Group Quarters Listing Sheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unit/Permit Listing Sheet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Permit Address List. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Permit Sketch Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B–8
B–10
B–11
B–12
B–13
B–14
B–15
B–16
Tables
3–1
3–2
3–3a
3–3b
3–4
3–5
3–6
3–7
3–8
10–1
10–2
10–3
11–1
11–2
11–3
12–1
14–1
14–2
14–3
14–4
14–5
16–1
16–2
16–3
Number and Estimated Population of Strata for 792-PSU Design by State . . . . . . . . . 3–6
Summary of Sampling Frames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3–10
Old Construction Within-PSU Sorts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3–11
Permit Frame Within-PSU Sort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3–12
Sampling Example Within a BPC for Any of the Three Old Construction Frames . . 3–12
Index Numbers Selected During Sampling for Code Assignment Examples . . . . . . . . 3–13
Example of Postsampling Survey Design Code Assignments Within a PSU . . . . . . . . 3–14
Example of Postsampling Operational Code Assignments Within a PSU . . . . . . . . . . . 3–14
Proportion of Sample in Common for 4-8-4 Rotation System . . . . . . . . . . . . . . . . . . . . . . . . 3–16
State First-Stage Factors for CPS 1990 Design (September - December 1995) . . . 10–5
Initial Cells for Hispanic/Non-Hispanic by Age and Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10–6
Initial Cells for Black/White/Other by Age, Race, and Sex. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10–6
Current Population Survey Supplements September 1994 - December 2001 . . . . . . 11–3
Summary of ADS Interview Month . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11–5
Summary of SCHIP Adjustment Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11–8
Bureau of Labor Statistics Data Products From the Current Population Survey . . . . 12–3
Parameters for Computation of Standard Errors for Estimates of Monthly Levels . . 14–5
Components of Variance for FSC Monthly Estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14–6
Effects of Weighting Stages on Monthly Relative Variance Factors. . . . . . . . . . . . . . . . . . 14–7
Effect of Compositing on Monthly Variance and Relative Variance Factors . . . . . . . . . 14–8
Design Effects for Total Monthly Variances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14–8
CPS Coverage Ratios by Age, Sex, Race, and Ethnicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–2
Components of Type A Nonresponse Rates, Annual Averages for 1993 - 1996 . . . . 16–3
Percentage of Households by Number of Completed Interviews During the 8
Months in the Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–4
16–4 Labor Force Status by Interview/Noninterview Status in Previous and Current
Month . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–5
16–5 Percentage of CPS Items With Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–5
16–6 Comparison of Responses to the Original Interview and the Reinterview . . . . . . . . . . . 16–6
16–7 Index of Inconsistency for Selected Labor Force Characteristics February - July
1994 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–6
16–8 Percentage of Households With Completed Interviews With Data Collected by
Telephone (CAPI Cases Only) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–7
16–9 Effect of Centralized Telephone Interviewing on Selected Labor Force
Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–8
16–10 Month-In-Sample Bias Indexes (and Standard Errors) in the CPS for Selected
Labor Force Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–9
16–11 Month-In-Sample Indexes in the CPS for Type A Noninterview Rates January December 1994 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–10
16–12 Percentage of CPS Labor Force Reports Provided by Proxy Reporters . . . . . . . . . . . . 16–10
B–1
Listings for Multiunit Addresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B–3
E–1
Reliability of CPS Estimators Under the Current Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E–1
F–1
Average Monthly Workload by Regional Office: 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F–3
H–1
The Proportion of Sample Cut and Expected CVs Before and After the January
1996 Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H–2
ix
Tables—Con.
H–2
H–3
H–4
H–5
H–6
H–7
H–8
H–9
H–10
J–1
J–2
J–3
J–4
J–5
J–6
January 1996 Reduction: Number and Estimated Population of Strata for
754-PSU Design for the Nine Reduced States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reduction Groups Dropped From SR Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
State First-Stage Factors for CPS January 1996 Reduction . . . . . . . . . . . . . . . . . . . . . . . . .
Components of Variance for FSC Monthly Estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Effects of Weighting Stages on Monthly Relative Variance Factors. . . . . . . . . . . . . . . . . .
Effect of Compositing on Monthly Variance and Relative Variance Factors . . . . . . . . .
Design Effects for Total Monthly Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Components of Variance for Composited Month-to-Month Change Estimates . . . . . .
Effect of Compositing on Month-to-Month Change Variance Factors . . . . . . . . . . . . . . . .
Size of SCHIP General Sample Increase and Effect on Detectable Difference by
State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Phase-in Table (First 11 Months of Sample Expansion) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Effect of SCHIP on National Labor Force Estimates: June 2001 . . . . . . . . . . . . . . . . . . . .
Effect of SCHIP on State Sampling Intervals and Coefficients of Variation. . . . . . . . . .
CPS Sample Housing Unit Counts With and Without SCHIP . . . . . . . . . . . . . . . . . . . . . . . .
Standard Errors on Percentage of Uninsured, Low-Income Children Compared at
Stages of the SCHIP Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
H–3
H–3
H–4
H–6
H–7
H–7
H–8
H–8
H–9
J–3
J–4
J–6
J–7
J–8
J–9
Appendix A. Maximizing Primary Sampling Unit (PSU) Overlap
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Adding a County to a PSU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dropping a County From a PSU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dropping and Adding PSU’s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A–1
A–3
A–3
A–4
A–5
Appendix B. Sample Preparation Materials
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unit Frame Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Area Frame Materials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Group Quarters Frame Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Permit Frame Materials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B–1
B–1
B–3
B–9
B–9
Appendix C. Maintaining the Desired Sample Size
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maintenance Reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C–1
C–1
Appendix D. Derivation of Independent Population Controls
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Population Universe for CPS Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Calculation of Population Projections for the CPS Universe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Adjustment of CPS Controls for Net Underenumeration in the 1990 Census. . . . . . . . . . . .
The Post-Enumeration Survey and Dual-System Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Monthly and Annual Revision Process for Independent Population Controls . . . . . . .
Procedural Revisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary List of Sources for CPS Population Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
D–1
D–3
D–4
D–20
D–20
D–22
D–22
D–23
D–23
x
Appendix E. State Model-Based Labor Force Estimation
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Time Series Component Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Diagnostic Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monthly Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
End-of-the-Year Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
E–1
E–1
E–4
E–4
E–4
E–4
E–5
E–6
Appendix F. Organization and Training of the Data Collection Staff
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Organization of Regional Offices/CATI Facilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Training Field Representatives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Field Representative Training Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Field Representative Performance Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Evaluating Field Representative Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F–1
F–1
F–1
F–1
F–3
F–4
Appendix G. Reinterview: Design and Methodology
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Response Error Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Quality Control Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reinterview Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
G–1
G–1
G–1
G–2
G–2
G–2
Appendix H. Sample Design Changes of the Current Population Survey:
January 1996
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Changes to the CPS Sample Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Revisions to CPS Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Estimation of Variances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
H–1
H–2
H–4
H–4
Appendix I. New Compositing Procedure
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Composite Estimation in the CPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Computing Composite Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A Note About Final Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
I–1
I–1
I–2
I–2
I–3
Appendix J. Changes to the Current Population Survey Sample in
July 2001
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
General Sample Increase in Selected States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Other Aspects of the SCHIP Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Effect of SCHIP Expansion on Standard Errors of Estimates of Uninsured
Low-Income Children. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
J–1
J–1
J–8
J–8
J–10
Acronyms .......................................................................... ACRONYMS–1
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . INDEX–1
xi
Summary of Changes
(Changes made to Current Population Survey Technical Paper 63 to Produce Technical Paper 63RV, March 2002)
Chapter 1.
Background
•
page 1-1, left column, fourth paragraph:1 added a footnote about the sample size increase
detailed in Appendix J.
Chapter 2.
History of the Current Population Survey
•
page 2-3, third paragraph of December 1971-March 1973: changed 1992 to 1972.2
•
page 2-4, April 1984: changed 1995 to 1985.
•
page 2-5: added a section January 1998 describing a two-step composite estimation
method.
•
page 2-5: added a section July 2001 describing the SCHIP.
Chapter 3.
Design of the Current Population Survey Sample
•
page 3-1, chapter heading: added a reference to Appendix J.
•
page 3-1, after second paragraph of INTRODUCTION: added text on the SCHIP.
•
page 3-11, Table 3-3a: corrected sorts.
Chapter 9.
Data Preparation
•
page 9-1, first paragraph of INDUSTRY AND OCCUPATION (I&O) CODING:
added a footnote about the increase of cases for coding because of the SCHIP.
Chapter 10.
Estimation Procedures for Labor Force Data
•
page 10-1, last paragraph of INTRODUCTION: added text about a new compositing
procedure detailed in Appendix I.
•
page 10-5, Table 10-1: changed 1999 in heading to 1990.
•
page 10-6, Table 10-3: inserted missing age category 55-59.
•
page 10-11, second paragraph of Estimates for States: added a footnote that all states
are now based on a model.
Chapter 11.
Current Population Survey Supplemental Inquiries
•
pages 11-1–11-3: made clarifications and revisions to improve readability.
•
pages 11-3–11-8, Annual Demographic Supplement (March Supplement): major
revisions due to more current methodologies and the inclusion of the SCHIP.
Chapter 14.
Estimation of Variance
•
page 14-5, Table 14-1: added a footnote that refers to Appendix H.
Chapter 16.
Quality Indicators of Nonsampling Errors
•
page 16-1, chapter heading: added a reference to a BLS internet site.
Appendix E.
State Model-Based Labor Force Estimation
•
page E-1, Table E-1: added a footnote about the SCHIP.
Appendix F.
Organization and Training of the Data Collection Staff
•
pages F-1 – F3, all sections: updated several of the numbers/percentages with 2001 data.
•
page F-3, Figure F-2: changed to Table F-1 and updated it.
•
page F-3, fourth paragraph of FIELD REPRESENTATIVE PERFORMANCE GUIDELINES:
changed CARMIN to CARMN.
1
Page number, column, and paragraph number are those from TP63. They indicate where the change begins.
Boldface indicates a change or addition from TP63 to TP63RV.
2
xii
Appendix G.
Reinterview: Design and Methodology
•
pages G-1–G-3, all sections: major revisions/deletions due to more current methodologies.
Appendix H.
Sample Design Changes of the Current Population Survey: January 1996
•
page H-1, chapter heading: added a reference to Appendix J.
Appendix J.
Changes to the Current Population Survey Sample in July 2001
•
This is an entirely new appendix, focusing on the changes that are collectively known as
the SCHIP sample expansion.
1–1
Chapter 1.
Background
The Current Population Survey (CPS), sponsored jointly
by the U.S. Census Bureau and the U.S. Bureau of Labor
Statistics (BLS), is the Nation’s primary source of labor
force statistics for the entire population. The CPS is the
source of numerous high-profile economic statistics including the Nation’s unemployment rate and provides data on a
wide range of issues relating to employment and earnings.
The CPS also collects extensive demographic data which
complement and enhance our understanding of labor market conditions in the Nation overall, among many different
population groups, the various states, and even substate
areas.
Although the labor market information is central to the
CPS, the survey provides a wealth of other social and
economic data that are widely used by social scientists in
both the public and private sectors. In addition, because of
its long history, the CPS has been a model for other
household surveys, both in the United States and in many
other countries.
Thus, the CPS is a source of information for both social
science research and the study of survey methodology.
This report aims to provide all users of the CPS with a
comprehensive guide to the survey. The report focuses on
labor force data because the timely and accurate collection
of those data remains the principal purpose of the survey.
The CPS is administered by the Census Bureau using a
scientifically selected sample of some 50,000 occupied
households.1 The fieldwork is conducted during the calendar week that includes the 19th of the month. The questions refer to activities during the prior week; that is, the
week that includes the 12th of the month.2 Households
from all 50 states and the District of Columbia are in the
survey for 4 consecutive months, out for 8, and then return
for another 4 months before leaving the sample permanently. This design ensures a high degree of continuity
from 1 month to the next (as well as over the year). The
4-8-4 sampling scheme has the added benefit of allowing
for the constant replenishment of the sample without
excessive response burden.
To be eligible to participate in the CPS, individuals must
be 15 years of age or over and not in the Armed Forces.
Persons in institutions, such as prisons, long-term care
hospitals, and nursing homes are, by definition, ineligible to
be interviewed in the CPS. In general, the BLS publishes
labor force data only for persons age 16 and over, since
those under 16 are substantially limited in their labor
market activities by compulsory schooling and child labor
laws. No upper age limit is used, and full-time students are
treated the same as nonstudents. One person generally
responds for all eligible members of the household. The
person who responds is called the ‘‘reference person’’ and
usually is the person who either owns or rents the housing
unit. If the reference person is not knowledgeable about the
employment status of the others in the household, attempts
are made to contact those individuals directly.
1
Beginning with July 2001, the sample size increased to 60,000
occupied households. (See Appendix J for details.)
2
In the month of December, the survey is often conducted 1 week
earlier to avoid conflicting with the holiday season.
The CPS questionnaire is a completely computerized
document that is administered by Census Bureau field
Within 2 weeks of the completion of these interviews, the
BLS releases the major results of the survey. Also included
in BLS’s analysis of labor market conditions are data from
a survey of nearly 400,000 employers (the Current Employment Statistics (CES) survey, conducted concurrently with
the CPS). These two surveys are complementary in many
ways. The CPS focuses on the labor force status (employed,
unemployed, not in labor force) of the working-age population and the demographic characteristics of workers and
nonworkers. The CES focuses on aggregate estimates of
employment, hours, and earnings for several hundred
industries that would be impossible to obtain with the same
precision through a household survey. The CPS reports on
individuals not covered in the CES, such as the self
employed, agricultural workers, and unpaid workers in a
family business. Information also is collected in the CPS
about persons who are not working.
In addition to the regular labor force questions, the CPS
often includes supplemental questions on subjects of interest to labor market analysts. These include annual work
activity and income, veteran status, school enrollment,
contingent employment, worker displacement, and job
tenure, among other topics. Because of the survey’s large
sample size and broad population coverage, a wide range
of sponsors use CPS supplements to collect data on topics
as diverse as expectation of family size, tobacco use,
computer use, and voting patterns. The supplements are
described in greater detail in Chapter 11.
1–2
representatives across the country through both personal
and telephone interviews. Additional telephone interviewing also is conducted from the Census Bureau’s three
centralized collection facilities in Hagerstown, Maryland;
Jeffersonville, Indiana; and Tucson, Arizona.
The labor force concepts and definitions used in the
CPS have undergone only slight modification since the
survey’s inception in 1940. Those concepts and definitions
are discussed in Chapter 5.
2–1
Chapter 2.
History of the Current Population Survey
INTRODUCTION
The Current Population Survey (CPS) has its origin in a
program established to provide direct measurement of
unemployment each month on a sample basis. There were
several earlier attempts to estimate the number of unemployed using various devices ranging from guesses to
enumerative counts. The problem of measuring unemployment became especially acute during the Economic Depression of the 1930s.
The Enumerative Check Census, taken as part of the
1937 unemployment registration, was the first attempt to
estimate unemployment on a nationwide basis using probability sampling. During the latter half of the 1930s, the
Work Projects Administration (WPA) developed techniques
for measuring unemployment, first on a local area basis
and later on a national basis. This research combined with
the experience from the Enumerative Check Census led to
the Sample Survey of Unemployment which was started in
March 1940 as a monthly activity by the WPA.
MAJOR CHANGES IN THE SURVEY: A
CHRONOLOGY
In August 1942, responsibility for the Sample Survey of
Unemployment was transferred to the Bureau of the Census, and in October 1943, the sample was thoroughly
revised. At that time, the use of probability sampling was
expanded to cover the entire sample, and new sampling
theory and principles were developed and applied to
increase the efficiency of the design. The households in the
revised sample were in 68 Primary Sampling Units (PSUs)
(see Chapter 3), comprising 125 counties and independent
cities. By 1945, about 25,000 housing units were designated for the sample, of which about 21,000 contained
interviewed households.
One of the most important changes in the CPS sample
design took place in 1954 when, for the same total budget,
the number of PSUs was expanded from 68 to 230, without
any change in the number of sample households. The
redesign resulted in a more efficient system of field organization and supervision and provided more information
per unit of cost. Thus the accuracy of published statistics
improved as did the reliability of some regional as well as
national estimates.
Since the mid-1950s, the CPS’s sample has undergone
major revision after every decennial census. The following
list chronicles the important modifications to the CPS
starting in the mid-1940s:
• July 1945. The CPS questionnaire was revised. The
revision consisted of the introduction of four basic employment status questions. Methodological studies showed
that the previous questionnaire produced results that
misclassified large numbers of part-time and intermittent
workers, particularly unpaid family workers. These groups
were erroneously reported as not active in the labor
force.
• August 1947. The selection method was revised. The
method of selecting sample units within a sample area
was changed so that each unit selected would have the
same basic weight. This change simplified tabulations
and estimation procedures.
• July 1949. Previously excluded dwelling places were
now covered. The sample was extended to cover special
dwelling places—hotels, motels, trailer camps, etc. This
led to improvements in the statistics, (i.e., reduced bias)
since residents of these places often have characteristics
different from the rest of the population.
• February 1952. Document sensing procedures were
introduced in the survey process. The CPS questionnaire
was printed on a document-sensing card. In this procedure, responses were recorded by drawing a line through
the oval representing the correct answer using an electrographic lead pencil. Punch cards were automatically
prepared from the questionnaire by document-sensing
equipment.
• January 1953. Ratio estimates now used data from the
1950 population census. Starting in January 1953, population data from the 1950 census were introduced into
the CPS estimation procedure. Prior to that date, the
ratio estimates had been based on 1940 census relationships for the first-stage ratio estimate, and 1940 population data were used to adjust for births, deaths, etc., for
the second-stage ratio estimate. In September 1953, a
question on ‘‘color’’ was added and the question on
‘‘veteran status’’ was deleted in the second-stage ratio
estimate. This change made it feasible to publish separate, absolute numbers for persons by race; whereas,
only the percentage of distributions had previously been
possible.
• July 1953. The 4-8-4 rotation system was introduced.
This sample rotation system was adopted to improve
measurement over time. In this system households are
interviewed for 4 consecutive months 1 year, leave the
2–2
sample for 8 months, and return for the same period of 4
months the following year. In the previous system, households were interviewed for 6 months and then replaced.
The 4-8-4 system provides some year-to-year overlap,
thus improving estimate of change on both a month-tomonth and year-to-year basis.
• September 1953. High speed electronic equipment was
introduced for tabulations. The introduction of electronic
calculation greatly increased timeliness and led to other
improvements in estimation methods. Other benefits
included the substantial expansion of the scope and
content of the tabulations and the computation of sampling variability. The shift to modern computers was
made in 1959. Keeping abreast of modern computing
has proved a continuous process, and to this day, the
Census Bureau is still updating and replacing its computer environment.
• February 1954. The number of PSUs was expanded to
230. The number of PSUs was increased from 68 to 230
while retaining the overall sample size of 25,000 designated housing units. The 230 PSUs consisted of 453
counties and independent cities. At the same time, a
substantially improved estimation procedure (See Chapter 10, Composite Estimation) was introduced.
Composite estimation took advantage of the large
overlap in the sample from month-to-month. These two
changes improved the reliability of most of the major
statistics by a magnitude that could otherwise be achieved
only by doubling the sample size.
• May 1955. Monthly questions on part-time workers were
added. Monthly questions exploring the reasons for
part-time work were added to the standard set of employment status items. In the past, this information had been
collected quarterly or less frequently and was found to be
valuable in studying labor market trends.
• July 1955. Survey week was moved. The CPS survey
week was moved to the calendar week containing the
12th day of the month to align the CPS time reference
with that of other employment statistics. Previously, the
survey week had been the calendar week containing the
8th day of the month.
• May 1956. The number of PSUs was expanded to 330.
The number of PSUs was expanded from 230 to 330.
The overall sample size also increased by roughly twothirds to a total of about 40,000 households units (about
35,000 occupied units). The expanded sample covered
638 counties and independent cities.
All of the former 230 PSUs were also included in the
expanded sample.
The expansion increased the reliability of the major
statistics by around 20 percent and made it possible to
publish more detailed statistics.
• January 1957. Employment status definition was changed.
Two relatively small groups of persons, both formerly
classified as employed ‘‘with a job but not at work,’’ were
assigned to new classifications. The reassigned groups
were (1) persons on layoff with definite instructions to
return to work within 30 days of the layoff date and (2)
persons waiting to start new wage and salary jobs within
30 days of the interview. Most of the persons in these two
groups were shifted to the unemployed classification.
The only exception was the small subgroup in school
during the survey week who were waiting to start new
jobs; these persons were transferred to ‘‘not in labor
force.’’ This change in definition did not affect the basic
question or the enumeration procedures.
• June 1957. Seasonal adjustment was introduced. Some
seasonally adjusted unemployment data were introduced
early in 1955. An extension of the data—using more
refined seasonal adjustment methods programmed on
electronic computers—was introduced in July 1957. The
new data included a seasonally adjusted rate of unemployment and trends of seasonally adjusted total employment and unemployment. Significant improvements in
methodology emerged from research conducted at the
Bureau of Labor Statistics and the Census Bureau in the
ensuing years.
• July 1959. Responsibility for CPS was moved between
agencies. Responsibility for the planning, analysis, and
publication of the labor force statistics from the CPS was
transferred to the BLS as part of a large exchange of
statistical functions between the Commerce and Labor
Departments. The Census Bureau continued to have
(and still has) responsibility for the collection and computer processing of these statistics, for maintenance of
the CPS sample, and for related methodological research.
Interagency review of CPS policy and technical issues
continues under the aegis of the Statistical Policy Division, Office of Management and Budget.
• January 1960. Alaska and Hawaii were added to the
population estimates and the CPS sample. Upon achieving statehood, Alaska and Hawaii were included in the
independent population estimates and in the sample
survey. This increased the number of sample PSUs from
330 to 333. The addition of these two states affected the
comparability of population and labor force data with
previous years. Another result was in an increase of
about 500,000 in the noninstitutional population of working age and about 300,000 in the labor force, four-fifths of
this in nonagricultural employment. The levels of other
labor force categories were not appreciably changed.
• October 1961. Conversion to the Film Optical Sensing
Device for Input to the Computer (FOSDIC) system. The
CPS questionnaire was converted to the FOSDIC type
used by the 1960 census. Entries were made by filling in
small circles with an ordinary lead pencil. The questionnaires were photographed to microfilm. The microfilms
were then scanned by a reading device which transferred
the information directly to computer tape. This system
permitted a larger form and a more flexible arrangement
2–3
of items than the previous document-sensing procedure
and did not require the preparation of punch cards. This
data entry system was used through December 1993.
• January 1963. New descriptive information was made
available. In response to recommendations of a review
committee, two new items were added to the monthly
questionnaire. The first was an item, formerly carried out
only intermittently, on whether the unemployed were
seeking full- or part-time work. The second was an
expanded item on household relationships, formerly included
only annually, to provide greater detail on the marital
status and household relationship of unemployed persons.
• March 1963. The sample and population data used in
ratio estimates were revised. From December 1961 to
March 1963, the CPS sample was gradually revised.
This revision reflected the changes in both population
size and distribution as established by the 1960 census.
Other demographic changes, such as the industrial mix
between areas, were also taken into account. The overall
sample size remained the same, but the number of PSUs
increased slightly to 357 to provide greater coverage of
the fast growing portions of the country. For most of the
sample, census lists replaced the traditional area sampling. These lists were developed in the 1960 census.
These changes resulted in further gains in reliability of
about 5 percent for most statistics. The census-based
updated population information was used in April 1962
for first- and second-stage ratio estimates.
• January 1967. The sample was expanded to 449 PSUs.
The CPS sample was expanded from 357 to 449 PSUs.
An increase in total budget allowed the overall sample
size to increase by roughly 50 percent to a total of about
60,000 housing units (52,500 occupied units). The expanded
sample had households in 863 counties and independent
cities with at least some coverage in every state.
This expansion increased the reliability of the major
statistics by about 20 percent and made it possible to
publish more detailed statistics.
The concepts of employment and unemployment
were modified. In line with the basic recommendations of
the President’s Committee to Appraise Employment and
Unemployment Statistics (Eckler, 1972), a several-year
study was conducted to develop and test proposed
changes in the labor force concepts. The principal
research results were implemented in January 1967.
The changes included a revised age cutoff in defining
the labor force; and new questions to improve the
information on hours of work, the duration of unemployment, and the self-employed. The definition of unemployment was also revised slightly. The revised definition of
unemployment led to small differences in the estimates
of level and month-to-month change.
• March 1968. Separate age/sex ratio estimation cells
were introduced for Negro and other races. Previously,
the second-stage ratio estimation used non-White and
White race categories by age groups and sex. The
revised procedures allowed for separate ratio estimates
for Negro and Other1 race categories.
This change amounts essentially to an increase in the
number of ratio estimation cells from 68 to 116.
• January 1971 and January 1972. 1970 census occupational classification was introduced. The questions on
occupation were made more comparable to those used
in the 1970 census by adding a question on major
activities or duties of current job. The new classification
was introduced into the CPS coding procedures in January 1971. Tabulated data were produced in the revised
version beginning in January 1972.
• December 1971-March 1973. Sample was expanded to
461 PSUs and data used in ratio estimation were updated.
From December 1971 to March 1973, the CPS sample
was revised gradually to reflect the changes in population size and distribution as described by the 1970
census. As part of an overall sample optimization, the
sample size was reduced slightly (from 60,000 to 58,000
housing units), but the number of PSUs increased to 461.
Also, the cluster design was changed from six nearby
(but not contiguous) to four usually contiguous households. This change was undertaken after research found
that smaller cluster sizes would increase sample efficiency.
Even with the reduction in sample size, this change
led to a small gain in reliability for most characteristics.
The noninterview adjustment and first stage ratio estimate adjustment were also modified to improve the
reliability of estimates for central cities and the rest of the
Standard Metropolitan Statistical Areas (SMSAs).
In January 1972, the population estimates used in the
second-stage ratio estimation were updated to the 1970
census base.
• January 1974. Inflation-deflation method was introduced
for deriving independent estimates of the population. The
derivation of independent estimates of the civilian noninstitutional population by age, race, and sex used in
second-stage ratio estimation in preparing the monthly
labor force estimates now used the inflation-deflation
method (see Chapter 10).
• September 1975. State supplementary samples were
introduced. An additional sample, consisting of about
14,000 interviews each month, was introduced in July
1975 to supplement the national sample in 26 states and
the District of Columbia. In all, 165 new PSUs were
involved. The supplemental sample was added to meet a
specific reliability standard for estimates of the annual
average number of unemployed persons for each state.
1
Other includes American Indian, Eskimo, Aleut, Asian, and Pacific
Islander.
2–4
In August 1976, an improved estimation procedure and
modified reliability requirements led to the supplement
PSUs being dropped from three states.
Thus, the size of the supplemental sample was reduced
to about 11,000 households in 155 PSUs.
• October 1978. Procedures for determining demographic
characteristics were modified. At this time, changes were
made in the collection methods for household relationship, race, and ethnicity. Race was now determined by
the respondent rather than by the interviewer.
Other modifications included the introduction of earnings questions for the two outgoing rotations. New items
focused on usual hours worked, hourly wage rate, and
usual weekly earnings. Earnings items were asked of
currently employed wage and salary workers.
• January 1979. A new two-level, first-stage ratio estimation procedure was introduced. This procedure was
designed to improve the reliability of metropolitan/nonmetropolitan
estimates.
Other newly introduced items were the monthly tabulation of children’s demographic data, including relationship, age, sex, race, and origin.
• September/October 1979. The final report of the National
Commission on Employment and Unemployment Statistics (NCEUS; ‘‘Levitan’’ Commission) (Executive Office of
the President, 1976) was issued. This report shaped
many of the future changes to the CPS.
• January 1980. To improve coverage about 450 households were added to the sample, increasing the number
of total PSUs to 629.
• May 1981. The sample was reduced by approximately
6,000 assigned households bringing the total sample
size to approximately 72,000 assigned households.
• January 1982. The race categories in the second-stage
ratio estimation adjustment were changed from White/NonWhite to Black/Non-Black. These changes were made to
eliminate classification differences in race that existed
between the 1980 census and the CPS. The change did
not result in notable differences in published household
data. Nevertheless, it did result in more variability for
certain ‘‘White,’’ ‘‘Black,’’ and ‘‘Other’’ characteristics.
As is customary, the CPS uses ratio estimates from
the most recent decennial census. Beginning in January
1982, these ratio estimates were based on findings from
the 1980 census. The use of the 1980 census-based
population estimates, in conjunction with the revised
second-stage adjustment, resulted in about a 2 percent
increase in the estimates for total civilian noninstitutional
population 16 years and over, civilian labor force, and
unemployed persons. The magnitude of the differences
between 1970 and 1980 census-based ratio estimates
affected the historical comparability and continuity of
major labor force series; therefore, the BLS revised
approximately 30,000 series back to 1970.
• November 1982. The question series on earnings was
extended to include items on union membership and
union coverage.
• January 1983. The occupational and industrial data
were coded using the 1980 classification systems. While
the effect on industry-related data was minor, the conversion was viewed as a major break in occupationrelated data series. The census developed a ‘‘list of
conversion factors’’ to translate occupation descriptions
based on the 1970 census-coding classification system
to their 1980 equivalents.
Most of the data historically published for the ‘‘Black
and Other’’ population group were replaced by data
which relate only to the ‘‘Black’’ population.
• October 1984. School enrollment items were added for
persons 16-24 years of age.
• April 1984. The 1970 census-based sample was phasedout through a series of changes that were completed by
July 1985. The redesigned sample used data from the
1980 census to update the sampling frame, took advantage of recent research findings to improve the efficiency
and quality of the survey, and used a state-based design
to improve the estimates for the states without any
change in sample size.
• September 1984. Collection of veteran’s data for females
was started.
• January 1985. Estimation procedures were changed to
use data from the 1980 census and new sample. The
major changes were to the second-stage adjustment
which replaced population estimates for ‘‘Black’’ and
‘‘Non-Black’’ (by sex and age groups) with population
estimates for ‘‘White,’’ ‘‘Black,’’ and ‘‘Other’’ population
groups. In addition, a separate, intermediate step was
added as a control to the Hispanic2 population. The
combined effect of these changes on labor force estimates and aggregates for most population groups was
negligible; however, the Hispanic population and associated labor force estimates were greatly affected and
revisions were made back to January 1980 to the extent
possible.
• June 1985. The CPS Computer Assisted Telephone
Interviewing (CATI) facility was opened at Hagerstown,
Maryland. A series of tests over the next few years were
conducted to identify and resolve the operational issues
associated with the use of CATI. Later tests focused on
CATI-related issues, such as data quality, costs, and
mode effects on labor force estimates. Samples used in
these tests were not used as part of the CPS.
• April 1987. First CATI cases were used in CPS monthly
estimates. Initially, CATI started with 300 cases a month.
As operational issues were resolved and new telephone
2
Hispanics may be of any race.
2–5
centers were opened—Tucson, Arizona (May 1992) and
Jeffersonville, Indiana (September 1994)—the CATI workload was gradually increased to about 9,200 cases a
month (January 1995).
• June 1990. The first of a series of experiments to test
alternative labor force questionnaires was started at the
Hagerstown Telephone Center. These tests used random digit dialing and were conducted in 1990 and 1991.
• July 1992. The CATI and Computer Assisted Personal
Interviewing (CAPI) Overlap (CCO) experiments began.
CATI and automated laptop versions of the revised CPS
questionnaire were used in a sample of about 12,000
households selected from the National Crime Victimization Survey sample. The experiment continued through
December 1993.
The CCO ran parallel with the official CPS. The
CCO’s main purpose was to gauge the combined effect
of the new questionnaire and computer-assisted data
collection. It is estimated that the redesign had no
statistically significant effect on the total unemployment
rate, but it did affect statistics related to unemployment,
such as the reasons for unemployment, the duration of
unemployment, and the industry and occupational distribution of the unemployed with previous work experience. It also is estimated that the redesign significantly
increased the employment-to-population ratio and the
labor force participation rate for women, but significantly
decreased the employment-to-population ratio for men.
Along with the changes in employment, it was estimated
that the redesign significantly influenced the measurement of characteristics related to employment, such as
the proportion of employment working part-time, the
proportion working part-time for economic reasons, the
number of individuals classified as self-employed, and
industry and occupational distribution of the employed.
• January 1994. A new questionnaire designed solely for
use in computer assisted interviewing was introduced in
the official CPS. Computerization allowed for the use of a
very complex questionnaire without increasing response
burden, increased consistency by reducing interviewer
error, permitted editing at time of interviewing, and allowed
for the use of dependent interviewing where information
reported in one month (industry/occupation, retired/disabled
statuses, and duration of unemployment) was confirmed
or updated in subsequent months.
Industry and occupation codes from the 1990 census
were introduced. Population estimates were converted
to 1990 census base for use in ratio estimation procedures.
• April 1994. The 16-month phase-in of the redesigned
sample based on the 1990 census began. The primary
purpose of this sample redesign was to maintain the
efficiency of the sampling frames. Once phased-in, this
resulted in a monthly sample of 56,000 eligible housing
units in 792 sample areas. The details of the 1990
sample redesign are described in Chapter 3.
• December 1994. Starting in December 1994, a new set
of response categories was phased in for the relationship
to reference person. This modification was directed at
individuals not formally related to the reference person to
identify unmarried partners in a household. The old
partner/roommate category was deleted and replaced
with the following categories: unmarried partner,
housemate/roommate, and roomer/boarder. This modification was phased in two rotation groups at a time and
was fully in place by March 1995. This change had no
effect on the family statistics produced by CPS.
• January 1996. The 1990 CPS design was changed
because of a funding reduction. The original reliability
requirements of the sample were relaxed, allowing a
reduction in the national sample size from roughly 56,000
eligible housing units to 50,000 eligible housing units.
The reduced CPS national sample contains 754 PSUs.
The details of the sample design changes as of January
1996 are described in Appendix H.
• January 1998. A new two-step composite estimation
method for the CPS was implemented (See Appendix I).
The first step involves computation of composite estimates for the main labor force categories, classified by
important demographic characteristics. The second adjusts
person weights, through a series of ratio adjustments, to
agree with the composite estimates, thus incorporating
the effect of composite estimation into the person weights.
This new technique provides increased operational simplicity for microdata users and improves the accuracy of
labor force estimates by using different compositing
coefficients for different labor force categories. The weighting adjustment method assures additivity while allowing
this variation in compositing coefficients.
• July 2001. Effective with the release of July 2001 data,
official labor force estimates from the CPS and Local
Area Unemployment Statistics (LAUS) program reflect
the expansion of the monthly CPS sample from about
50,000 to about 60,000 eligible households. This expansion of the monthly CPS sample was one part of the
Census Bureau’s plan to meet the requirements of the
State Children’s Health Insurance Program (SCHIP)
legislation. The SCHIP legislation requires the Census
Bureau to improve state estimates of the number of
children who live in low-income families and lack health
insurance. These estimates are obtained from the Annual
Demographic Supplement to the CPS. In September
2000, the Census Bureau began expanding the monthly
CPS sample in 31 states and the District of Columbia.
States were identified for sample supplementation based
on the standard error of their March estimate of lowincome children without health insurance. The additional
10,000 households were added to the sample over a
3-month period. The BLS chose not to include the
2–6
additional households in the official labor force estimates, however, until it had sufficient time to evaluate the
estimates from the 60,000 household sample. See Appendix J, Changes to the Current Population Survey Sample
in July 2001, for details.
REFERENCES
Eckler, R.A. (1972), U.S. Census Bureau, New York:
Praeger.
Executive Office of the President, Office of Management
and Budget, Statistical Policy Division (1976), Federal
Statistics: Coordination, Standards, Guidelines: 1976,
Washington, DC: Government Printing Office.
3–1
Chapter 3.
Design of the Current Population Survey Sample
(See Appendix H for sample design changes as of January 1996 and Appendix J for changes in July 2001.)
INTRODUCTION
For more than five decades, the Current Population
Survey (CPS) has been one of the major sources of
up-to-date information on the labor force and demographic
characteristics of the U.S. population. Because of the
CPS’s importance and high profile, the reliability of the
estimates has been evaluated periodically. The design has
often been under constant and close scrutiny in response
to demand for new data and to improve the reliability of the
estimates by applying research findings and new types of
information (especially census results). All changes are
implemented with concern for minimizing cost and maximizing comparability of estimates across time. A sample
redesign takes place after each census. The most recent
decennial revision which incorporated new information
from the 1990 census was, in the main, complete as of July
1995. Thus, this chapter describes the CPS sample design
as of July 1995.
In January 1996, the CPS design was again modified
because of funding reductions. This time, the redesign was
restricted to the most populous states where survey requirements were relaxed compared to earlier years. These
changes in the sample are not reflected in the main body of
this report, but the reader is referred to Appendix H which
provides an up-to-date and contemporary description of
the changes introduced in 1996. Periodic modifications to
the CPS design are occasionally needed to respond to
growth of the housing stock. Appendix C discusses how
this type of design modification is implemented. Any changes
to the CPS design that postdate publication of the present
document will be described separately in future appendices.
Effective with the release of July 2001 data, official labor
force estimates from the CPS and Local Area Unemployment Statistics (LAUS) program reflect the expansion of
the monthly CPS sample. The State Children’s Health
Insurance Program (SCHIP) legislation requires the Census Bureau to improve state estimates of the number of
children who live in low-income families and lack health
insurance. These estimates are obtained from the Annual
Demographic Supplement to the CPS. States were identified for sample supplementation based on the standard
error of their March estimate of low-income children without
health insurance. See Appendix J for details.
This chapter is directed to a general audience and
presents many topics with varying degrees of detail. The
following section provides a broad overview of the CPS
design and is recommended for all readers. Later sections
of this chapter provide a more in-depth description of the
CPS design and are recommended for readers who require
greater detail.
SURVEY REQUIREMENTS AND DESIGN
Survey Requirements
The following list briefly describes the major characteristics of the CPS sample as of July 1995:
1. The CPS sample is a probability sample.
2. The sample is designed primarily to produce national
and state estimates of labor force characteristics of the
civilian noninstitutional population 16 years of age and
older (CNP16+).
3. The CPS sample consists of independent samples in
each state and the District of Columbia. In other words,
each state’s sample is specifically tailored to the
demographic and labor market conditions that prevail
in that particular state. California and New York State
are further divided into two substate areas that also
have independent designs: the Los Angeles-Long Beach
metropolitan area and the rest of California; New York
City and the rest of New York State. Since the CPS
design consists of independent designs for the states
and substate areas, it is said to be state-based.
4. Sample sizes are determined by reliability requirements which are expressed in terms of the coefficient
of variation, or CV. The CV is a relative measure of the
sampling error and is calculated as sampling error
divided by the expected value of the given characteristic. The specified CV for the monthly unemployment
level for the nation, given a 6 percent unemployment
rate, is 1.8 percent. The 1.8 percent CV is based on
the requirement that a difference of 0.2 percent in the
unemployment rate for two consecutive months be
significant at the 0.10 level.
5. The specified CV for the monthly unemployment level
for 11 states, given a 6 percent unemployment rate, is
8 percent.1 The specified CV for the monthly unemployment level for the California and New York substate areas, given a 6 percent unemployment rate, is 9
1
The 11 states are California, Florida, Illinois, Massachusetts, Michigan, New Jersey, New York, North Carolina, Ohio, Pennsylvania, and
Texas.
3–2
percent. This latter specification leads to California and
New York State having CVs somewhat less than 8
percent.
6. The required CV on the annual average unemployment level for the other 39 states and the District of
Columbia, given a 6 percent unemployment rate, is 8
percent.
Overview of Survey Design
The CPS sample is a multistage stratified sample of
approximately 56,000 housing units from 792 sample areas
designed to measure demographic and labor force characteristics of the civilian noninstitutional population 16
years of age and older. The CPS samples housing units
from lists of addresses obtained from the 1990 Decennial
Census of Population and Housing. These lists are updated
continuously for new housing built after the 1990 census.
The first stage of sampling involves dividing the United
States into primary sampling units (PSUs) — most of which
comprise a metropolitan area, a large county, or a group of
smaller counties. Every PSU falls within the boundary of a
state. The PSUs are then grouped into strata on the basis
of independent information, that is, information obtained
from the decennial census or other sources.
The strata are constructed so that they are as homogeneous as possible with respect to labor force and other
social and economic characteristics that are highly correlated with unemployment. One PSU is sampled per stratum. The probability of selection for each PSU in the
stratum is proportional to its population as of the 1990
census.
In the second stage of sampling, a sample of housing
units within the sample PSUs is drawn. Ultimate sampling
units (USUs) are clusters of about four housing units. The
bulk of the USUs sampled in the second stage consists of
sets of addresses which are systematically drawn from
sorted lists of addresses of housing units prepared as part
of the 1990 census. Housing units from blocks with similar
demographic composition and geographic proximity are
grouped together in the list. In parts of the United States
where addresses are not recognizable on the ground,
USUs are identified using area sampling techniques. Occasionally, a third stage of sampling is necessary when actual
USU size is extremely large. A final addition to the USUs is
a sample of building permits, which compensates for the
exclusion of construction since 1990 in the list of addresses
in the 1990 census.
Each month, interviewers collect data from the sample
housing units. A housing unit is interviewed for 4 consecutive months and then dropped out of the sample for the
next 8 months and is brought back in the following 4
months. In all, a sample housing unit is interviewed eight
times. Households are rotated in and out of the sample in
a way that improves the accuracy of the month-to-month
and year-to-year change estimates. The rotation scheme
ensures that in any 1 month, one-eighth of the housing
units are interviewed for the first time, another eighth is
interviewed for the second time, and so on. That is, after
the first month, 6 of the 8 rotation groups will have been in
the survey for the previous month — there will always be a
75 percent month-to-month overlap. When the system has
been in full operation for 1 year, 4 of the 8 rotation groups
in any month will have been in the survey for the same
month, 1 year ago; there will always be a 50 percent
year-to-year overlap. This rotation scheme fully upholds
the scientific tenets of probability sampling, so that each
month’s sample produces a true representation of the
target population. Also, this rotation scheme is considered
better than other candidate schemes. For example, undue
reporting burden expected of survey respondents if they
were to constitute a permanent panel is avoided. The
properties of the rotation system also show that it could be
used to reduce sampling error by use of a composite
estimation procedure2 and, at slight additional cost, by
increasing the representation in the sample of USUs with
unusually large numbers of housing units.
Each state’s sample design ensures that most housing
units within a state have the same overall probability of
selection. Because of the state-based nature of the design,
sample housing units in different states have different
overall probabilities of selection. It is true that if we considered only the national level, a more efficient design would
result from using the same overall probabilities for all
states. Nevertheless, the current system of state-based
designs ensures that both the state and national reliability
requirements are met.
FIRST STAGE OF THE SAMPLE DESIGN
The first stage of the CPS sample design is the selection
of counties. The purpose of selecting a subset of counties
instead of having all counties in the sample is to reduce
travel costs for the field representatives. Two features of
the first-stage sampling are: (1) to ensure that sample
counties represent other counties with similar labor force
characteristics that are not selected and (2) to ensure that
each field representative is allotted a manageable workload in his/her sample area.
The first stage-sample selection is carried out in three
major steps:
1. Definition of the PSUs.
2. Stratification of the PSUs within each state.
3. Selection of the sample PSUs in each state.
2
The complete estimation procedure results in significant reduction in
the sampling error of estimates of level and change for most items. This
procedure depends on the fact that data from previous months are usually
highly correlated with the corresponding estimates for the current month.
3–3
Definition of the Primary Sampling Units
PSUs are delineated in such a way that they encompass
the entire United States. The land areas within each PSU
are made reasonably compact so they can be traversed by
an interviewer without incurring unreasonable costs. The
population is as heterogeneous with regard to labor force
characteristics as can be made consistent with the other
constraints. Strata are constructed that are homogenous in
terms of labor force characteristics to minimize betweenPSU variance. Between-PSU variance is a component of
total variance which arises from selecting a sample of
PSUs rather than selecting housing units from all PSUs. In
each stratum, a PSU is selected that is representative of
the other PSUs in the same stratum. When revisions are
made in the sample each decade, a procedure used for
reselection of PSUs maximizes the overlap in the sample
PSUs with the previous CPS sample (see Appendix A).
Most PSUs are groups of contiguous counties rather
than single counties. A group of counties is more likely to
have diverse labor force characteristics rather than a single
county. Limits are placed on the geographic size of a PSU
to contain the distance a field representative must travel.
After some empirical research in the late 1940s to help
establish rules, the PSUs were initially established in late
1949 and early 1950. The original definitions were subsequently modified and now conform to the rules listed below.
Rules for Defining PSUs
1. PSUs are contained within state boundaries.
2. Metropolitan areas are defined as separate PSUs
using projected 1990 Metropolitan Statistical Area
(MSA) definitions. (An MSA is defined to be at least
one county.) If an MSA straddles state boundaries,
each state-MSA intersection is a separate PSU.3
3. For most states, PSUs are either one county or two or
more contiguous counties. For the New England states4
and part of Hawaii, minor civil divisions (towns or
townships) define the PSUs. In some states, county
equivalents are used: cities, independent of any county
organization, in Maryland, Missouri, Nevada, and Virginia; parishes in Louisiana; and boroughs and census
divisions in Alaska.
4. The area of the PSU should not exceed 3,000 square
miles except in cases where a single county exceeds
the maximum area.
3
Final MSA definitions were not available from the Office of Management and Budget when PSUs were defined. Fringe counties having a
good chance of being in final MSA definitions are separate PSUs. Most
projected MSA definitions are the same as final MSA definitions (Executive Office of the President, 1993).
4
The New England states are Rhode Island, Connecticut, Massachusetts, Maine, New Hampshire, and Vermont.
5. The population of the PSU is at least 7,500 except
where this would require exceeding the maximum area
specified in number 4.
6. In addition to meeting the limitation on total area,
PSUs are formed to limit extreme length in any direction and to avoid natural barriers within the PSU.
Combining counties into PSUs. The PSU definitions are
reviewed each time the CPS sample design is revised.
Before 1980, almost all changes in the composition of the
PSUs were made to reflect changes in definitions of MSAs.
For 1980, revised PSU definitions reflect new MSA definitions and ensure that the PSU definitions were compatible
with a state-based sample design. For 1990, revised PSU
definitions reflect changes in MSA definitions and make the
PSU definitions more consistent with those used by the
other Census Bureau demographic surveys. The following
are steps for combining counties, county equivalents, and
independent cities into PSUs for 1990.
1. The 1980 PSUs are evaluated by incorporating into
the PSU definitions those counties comprising MSAs
that are new or have been redefined.
2. Any single county is classified as a separate PSU,
regardless of its 1990 population, if it exceeds the
maximum area limitation deemed practical for interviewer travel.
3. Other counties within the same state are examined to
determine whether they might advantageously be combined with contiguous counties without violating the
population and area limitations.
4. Contiguous counties with natural geographic barriers
between them are placed in separate PSUs to reduce
the cost of travel within PSUs.
5. The proposed combinations are reviewed. Although
personal judgment can have no place in the actual
selection of sample units, (known probabilities of selection can be achieved only through a random selection
process) there are a large number of ways in which a
given population can be structured and arranged prior
to sampling. Personal judgment legitimately plays an
important role in devising an optimal arrangement; that
is, one designed to minimize the variances of the
sample estimates subject to cost constraints.
These steps result in 2,007 CPS PSUs in the United States
from which to draw a sample.
Stratification of Primary Sampling Units
The CPS sample design calls for combining PSUs into
strata within each state and selecting one PSU from each
stratum. For this type of sample design, sampling theory
3–4
suggests forming strata with approximately equal population sizes. When the design is self-weighting (same sampling fraction in all strata) and one field representative is
assigned to each sample PSU, equal stratum sizes also
have the advantage of providing equal field representative
workloads (at least during the early years of each decade,
before population growth and migration significantly affect
the PSU population sizes). The objective of the stratification, therefore, is to group PSUs with similar characteristics
into strata having approximately equal 1990 populations.
Sampling theory also dictates that highly populated
PSUs should be selected for sample with certainty. The
rationale is that some PSUs exceed or come close to the
stratum size needed for equalizing stratum sizes. These
PSUs are designated as self-representing (SR); that is,
each of the SR PSUs is treated as a separate stratum and
is included in the sample.
The following describes the steps for stratifying PSUs for
the 1990 redesign.
1.
The PSUs required to be SR are identified if the PSU
meets one of the following criteria:
a. The PSU belongs to one of the 150 MSAs with the
largest populations in the 1990 census or the PSU
contains counties which had a good chance of
joining one of these 150 MSAs under final MSA
definitions.
b. The PSU belongs to an MSA that was SR for the
1980 design and among the 150 largest following
the 1980 census.
2.
The remaining PSUs are grouped into nonself-representing
(NSR) strata within state boundaries by adhering to
the following criteria:
a. Roughly equal-sized NSR strata are formed within
a state.
b. NSR strata are formed so as to yield reasonable
field representative workloads in an NSR PSU of
roughly 45 to 60 housing units. The number of NSR
strata in a state is a function of 1990 population,
civilian labor force, state CV, and between-PSU
variance on the unemployment level. (Workloads in
NSR PSUs are constrained because one field
representative must canvass the entire PSU. No
such constraints are placed on SR PSUs.)
c. NSR strata are formed with PSUs homogeneous
with respect to labor force and other social and
economic characteristics that are highly correlated
with unemployment. This helps to minimize the
between-PSU variance.
d. Stratification is performed independently of previous CPS sample designs.
Key variables used for stratification are:
• Number of male unemployed.
• Number of female unemployed.
• Number of families with female head of household.
• Ratio of occupied housing units with three or
more persons, of all ages, to total occupied
housing units.
In addition to these, a number of other variables
such as industry and wage variables obtained from
the Bureau of Labor Statistics are used for some
states. The number of stratification variables in a
state ranges from 3 to 12.
Table 3–1 summarizes the number of SR and NSR
strata in each state. (The other columns of the table are
discussed in later sections of this chapter.)
The algorithm for implementing the NSR stratification
criteria for the 1980 and 1990 sample designs is a modified
version of the Friedman-Rubin clustering algorithm (Kostanich, 1981). The algorithm consists of three basic steps:
hillclimbing, size adjustment, and an exchange pass. For
each state, the algorithm identifies a stratification which
meets two criteria: (1) all strata are about the same size
and (2) the value of the objective function—a scaled total
between-PSU variance for all the stratification variables—is
relatively small. Each of the algorithm’s three steps assigns
slightly different priorities to criteria (1) and (2). Before the
start of the first step, the program groups the PSUs within
a state into randomly defined strata. The algorithm then
‘‘swaps’’ PSUs between strata to reduce size disparity
between the strata or to decrease the value of the objective
function. The hillclimbing procedure moves PSUs from
stratum to stratum, subject to loose size constraints, in
order to minimize the between-PSU variance for stratification variables.5 The size adjustment tightens size constraints and adjusts stratum sizes by making moves that
lead to the smallest increases in between-PSU variance.
With tight size constraints, the exchange pass seeks to
further reduce between-PSU variance by exchanging PSUs
between strata.
The algorithm is run several times allowing stratum sizes
to vary by differing degrees. A final stratification is chosen
which minimizes, to the extent possible, variability in stratum workloads and total between-PSU variance for all
stratification variables for the state. If a stratification results
in an NSR PSU being placed in a stratum by itself, the PSU
is then SR. After the strata are defined, some state sample
sizes are adjusted to bring the national CV for unemployment level down to 1.8 percent assuming a 6 percent
unemployment rate. (The stratification procedure for Alaska
takes into account expected interview cost and betweenPSU variance (Ludington, 1992).)
5
Between-PSU variance is the component of total variance arising
from selecting a sample of PSUs from all possible PSUs. For this
stratification process, the between-PSU variance was calculated using
1990 census data for the stratification variables.
3–5
A consequence of the above stratification criteria is that
states that are geographically small, mostly urban, or
demographically homogeneous are entirely SR. These
states are Connecticut, Delaware, Massachusetts, New
Hampshire, New Jersey, Rhode Island, Vermont, and the
District of Columbia.
Selection of Sample Primary Sampling Units
Each SR PSU is in the sample by definition. As shown in
Table 3–1, there are 432 SR PSUs. In each of the
remaining 360 NSR strata, one PSU is selected for the
sample following the guidelines described next.
At each sample redesign of the CPS, it is important to
minimize the cost of introducing a new set of PSUs.
Substantial investment has been made in the hiring and
training of field representatives in the existing sample
PSUs. For each PSU dropped from the sample and
replaced by another in the new sample, the expense of
hiring and training a new field representative must be
accepted. Furthermore, there is a temporary loss in accuracy of the results produced by new and relatively inexperienced field representatives. Concern for these factors is
reflected in the procedure used for selecting PSUs.
Objectives of the selection procedure. The selection of
the sample of NSR PSUs is carried out within the strata
using the 1990 population. The selection procedure accomplishes the following objectives:
1. Select one sample PSU from each stratum with probability proportional to the 1990 population.
2. Retain in the new sample the maximum number of
sample PSUs from the 1980 design sample.
Using the Maximum Overlap procedure described in
Appendix A, one PSU is selected per stratum with probability proportional to its 1990 population. This procedure
uses mathematical programming techniques to maximize
the probability of selecting PSUs that are already in sample
while maintaining the correct overall probabilities of selection.
sample selection.6 The first-stage variance is called the
between-PSU variance and the second-stage variance is
called the within-PSU variance. The square of the state CV,
or the relative variance, on the unemployment level is
expressed as
CV2 ⫽
where
␴b2
␴w2
E(x)
␴b2 ⫹ ␴w2
关E共x兲兴2
共3.1兲
= between-PSU variance contribution to
the variance of the state unemployment
level estimator.
= within-PSU variance contribution to the
variance of the state unemployment level
estimator.
= the expected value of the unemployment
level for the state.
The term, ␴w2, can be written as the variance assuming a
binomial distribution from a simple random sample multiplied by a design effect
␴w2 ⫽
where
N
p
N2 p q 共deff兲
n
= the civilian noninstitutional population, 16
years of age and older (CNP16+), for the
state.
= proportion of unemployed in the CNP16+
x
for the state, or N.
Substituting.
q = 1 – p.
n = the state sample size.
deff = the state within-PSU design effect. This is a factor
accounting for the difference between the variance calculated from a multistage stratified sample
and that from a simple random sample.
This formula can be rewritten as
␴w2 ⫽ SI 共x q兲 共deff兲
共3.2兲
where
Calculation of overall state sampling interval. After
stratifying the PSUs within the states, the overall sampling
interval in each state is computed. The overall state
sampling interval is the inverse of the probability of selection of each housing unit in a state for a self-weighting
design. By design, the overall state sampling interval is
fixed, but the state sample size is not fixed allowing growth
of the CPS sample because of housing units built after the
1990 census. (See Appendix C for details on how the
desired sample size is maintained.)
The state sampling interval is designed to meet the
requirements for the variance on an estimate of the unemployment level. This variance can be thought of as a sum of
variances from the first stage and the second stage of
SI
N
= the state sampling interval, or n .
Substituting (3.2) into (3.1) and rewriting in terms of the
state sampling interval gives
SI ⫽
CV2x2 ⫺ ␴b2
x q deff
6
The variance of an estimator, u, based on a two-stage sample has the
general form
Var共u兲 ⫽ VarIEII共u兩 set of sample PSUs兲 ⫹ EIVarII共u兩 set of sample PSUs兲
where I and II represent the first and second stage designs, respectively.
The left term represents the between-PSU variance,␴b2. The right term
represents the within-PSU variance, ␴w2.
3–6
Table 3–1. Number and Estimated Population of Strata for 792-PSU Design by State
Self-representing (SR)
State
Number of strata
Nonself-representing (NSR)
Estimated
population1 Number of strata
Estimated
population1
Overall
sampling
interval
Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alabama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arkansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of California. . . . . . . . . . . . . . . . . . . . . . . . . . .
Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Connecticut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
432
4
5
3
4
21
1
20
5
20
3
141,876,295
1,441,118
287,192
2,188,880
612,369
20,866,697
6,672,035
14,194,662
1,839,521
2,561,010
509,330
360
10
5
4
13
5
0
5
5
0
0
45,970,646
1,604,901
91,906
544,402
1,151,315
1,405,500
0
1,405,500
629,640
0
0
2,060
2,298
336
2,016
1,316
2,700
1,691
3,132
1,992
2,307
505
District of Columbia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Indiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Iowa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kentucky. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
20
9
3
8
11
9
5
3
9
486,083
9,096,732
2,783,587
778,364
416,284
6,806,874
2,215,745
710,910
920,819
1,230,062
0
8
9
1
8
12
8
11
9
9
0
1,073,818
2,038,631
49,720
302,256
1,821,548
1,949,996
1,372,871
906,626
1,546,770
356
2,176
3,077
769
590
1,810
3,132
1,582
1,423
2,089
Louisiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maryland. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minnesota. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Missouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Montana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nebraska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
8
4
31
12
4
6
7
8
2
1,638,220
685,937
3,310,546
4,719,188
5,643,817
2,137,810
540,351
2,394,137
366,320
557,203
9
4
2
0
11
7
14
6
8
9
1,403,969
247,682
352,766
0
1,345,074
1,119,797
1,334,898
1,457,616
221,287
608,513
2,143
838
3,061
954
1,396
2,437
1,433
3,132
431
910
Nevada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Hampshire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of New York . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Dakota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ohio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
13
11
6
13
1
12
14
4
13
819,424
846,029
6,023,359
685,543
12,485,029
5,721,495
6,763,534
3,089,176
232,865
6,238,600
2
0
0
8
7
0
7
23
9
13
98,933
0
0
410,929
1,414,548
0
1,414,548
1,973,074
235,364
1,958,138
961
857
1,221
867
1,709
1,159
2,093
1,095
363
1,653
Oklahoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oregon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rhode Island . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
South Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
South Dakota. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tennessee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Texas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Utah. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
4
14
5
7
5
8
22
2
11
1,244,920
1,396,261
7,704,963
784,090
1,434,621
211,146
2,502,671
8,779,997
888,524
428,263
11
6
11
0
7
11
6
20
3
0
1,092,513
761,794
1,507,946
0
1,161,298
291,546
1,219,915
3,613,170
251,746
0
1,548
1,904
1,757
687
2,291
376
3,016
2,658
958
410
Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wisconsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wyoming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
5
10
7
8
3,093,947
2,437,454
803,797
1,773,109
227,401
6
6
9
9
6
1,612,667
1,216,557
581,544
1,889,141
98,321
3,084
2,999
896
2,638
253
1
Estimate of civilian noninstitutional population 16 years of age and older based on preliminary 1990 census counts.
3–7
Generally, this overall state sampling interval is used for
all strata in a state yielding a self-weighting state design.
(In some states, the sampling interval is adjusted in certain
strata to equalize field representative workloads.)
When computing the sampling interval for the current
CPS sample, a 6 percent state unemployment rate is
assumed for 1995. The results are given in Table 3–1,
which was provided earlier.
SECOND STAGE OF THE SAMPLE DESIGN
The second stage of the CPS sample design is the
selection of sample housing units within PSUs. The objectives of within-PSU sampling are to:
1. Select a probability sample that is representative of the
total civilian, noninstitutional population.
2. Give each housing unit in the population one and only
one chance of selection, with virtually all housing units
in a state having the same overall chance of selection.
3. For the sample size used, keep the within-PSU variance on labor force statistics (in particular, unemployment) at as low a level as possible, subject to response
burden, costs, and other constraints.
4. Select enough within-PSU sample for additional samples
that will be needed before the next decennial census.
5. Put particular emphasis on providing reliable estimates of monthly levels and change over time of labor
force items.
1990 and later is used to supplement the census data.
These data are collected via the Building Permit Survey,
which is an ongoing survey conducted by the Census
Bureau. Therefore, a list sample of census addresses,
supplemented by a sample of building permits, is used in
most of the United States. However, where city-type street
addresses from the 1990 census do not exist, or where
residential construction does not need or require building
permits, area samples are sometimes necessary. (See the
next section for more detail on the development of the
sampling frames.)
These sources provide sampling information for numerous demographic surveys conducted by the Census Bureau.7
In consideration of respondents, sampling methodologies
are coordinated among these surveys to ensure a sampled
housing unit is selected for one survey only. Consistent
definition of sampling frames allows for the development of
separate, optimal sampling schemes for each survey. The
general strategy for each survey is to sort and stratify all
the elements in the sampling frame (eligible and not
eligible) to satisfy individual survey requirements, select a
systematic sample, and remove the selected sample from
the frame. Sample is selected for the next survey from what
remains. Procedures are developed to determine eligibility
of sample cases at the time of interview for each survey.
This coordinated sampling approach is computer intensive
and was not possible in previous redesigns.8
Development of Sampling Frames
Overview of Sampling Sources
Results from the 1990 census, the Building Permit
Survey, and the relationship between these two sources
are used in developing sampling frames. Four frames are
created: the unit frame, the area frame, the group quarters
frame, and the permit frame. The unit, area, and group
quarters frames are collectively called old construction. To
describe frame development methodology, several terms
must be defined.
Two types of living quarters were defined for the census.
The first type is a housing unit. A housing unit is a group of
rooms or a single room occupied as a separate living
quarter or intended for occupancy as a separate living
quarter. A separate living quarter is one in which the
occupants live and eat separately from all other persons on
the property and have direct access to their living quarter
To accomplish the objectives of within-PSU sampling,
extensive use is made of data from the 1990 Decennial
Census of Population and Housing and the Building Permit
Survey. The 1990 census collected information on all living
quarters existing as of April 1, 1990, including characteristics of living quarters as well as the demographic composition of persons residing in these living quarters. Data on
the economic well-being and labor force status of individuals were solicited for about 1 in 6 housing units. However,
since the census does not cover housing units constructed
since April 1, 1990, a sample of building permits issued in
7
CPS sample selection was coordinated with the following demographic surveys in the 1990 redesign: the American Housing Survey Metropolitan Sample, the American Housing Survey - National sample,
the Consumer Expenditure Survey - Diary sample, the Consumer Expenditure Survey - Quarterly sample, the Current Point of Purchase Survey,
the National Crime Victimization Survey, the National Health Interview
Survey, the Rent and Property Tax Survey, and the Survey of Income and
Program Participation.
8
This sampling strategy is unbiased because if a random selection is
removed from a frame, the part of the frame that remains is a random
subset. Also, the sample elements selected and removed from each
frame for a particular survey have similar characteristics as the elements
remaining in the frame.
USUs are the sample units selected during the second
stage of the CPS sample design. As discussed earlier in
this chapter, most USUs consist of a geographically compact cluster of approximately four addresses, corresponding to four housing units at the time of the census. Use of
housing unit clusters lowers travel costs for field representatives. Clustering slightly increases within-PSU variance
of estimates for some labor force characteristics since
respondents within a compact cluster tend to have similar
labor force characteristics.
3–8
from the outside or through a common hall or lobby as
found in apartment buildings. A housing unit may be
occupied by a family or one person, as well as by two or
more unrelated persons who share the living quarter. About
98 percent of the population counted in the 1990 census
resided in housing units.
The second type of living quarter is a group quarters. A
group quarters is a living quarter where residents share
common facilities or receive formally authorized care.
Examples include college dormitories, retirement homes,
and communes. For some group quarters, such as fraternity and sorority houses and certain types of group houses,
a group quarters is distinguished from a housing unit if it
houses ten or more unrelated people. The group quarters
population is classified as institutional or noninstitutional
and as military or civilian. CPS targets only the civilian
noninstitutional population residing in group quarters. Military and institutional group quarters are included in the
group quarters frame and given a chance of selection in
case of conversion to civilian noninstitutional housing by
the time it is scheduled for interview. Less than 2 percent of
the population counted in the 1990 census resided in group
quarters.
Old Construction Frames
Old construction consists of three sampling frames: unit,
area, and group quarters. The primary objectives in constructing the three sampling frames are maximizing the use
of census information to reduce variance of estimates,
ensuring adequate coverage, and minimizing cost. The
sampling frames used in a particular geographic area take
into account three major address features:
1. Type of living quarters — housing units or group
quarters.
2. Completeness of addresses — complete or incomplete.
3. Building permit office coverage — covered or not
covered.
An address is considered complete if it describes a
specific location; otherwise, the address is considered
incomplete. (When the 1990 census addresses cannot be
used to locate sample units, area listings must be performed in those areas before sample units can be selected
for interview. See Chapter 4 for more detail.) Examples of
a complete address are city delivery types of mailing
addresses composed of a house number, street name, and
possibly a unit designation, such as ‘‘1599 Main Street’’ or
‘‘234 Elm Street, Apartment 601.’’ Examples of incomplete
addresses are addresses composed of postal delivery
information without indicating specific locations, such as
‘‘PO Box 123’’ or ‘‘Box 4’’ on a rural route. Housing units in
complete blocks covered by building permit offices are
assigned to the unit frame. Group quarters in complete
blocks covered by building permit offices are assigned to
the group quarters frame. Other blocks are assigned to the
area frame.
Unit frame. The unit frame consists of housing units in
census blocks that contain a very high proportion of
complete addresses and are essentially covered by building permit offices. The unit frame covers most of the
population. (Although building permit offices cover nearly
all blocks in the unit frame, a few exceptions may slightly
compromise CPS coverage of the target population (see
Chapter 16)). A USU in the unit frame consists of a
compact cluster of four addresses, which are identified
during sample selection. The addresses, in most cases,
are those for separate housing units. However, over time
some buildings may be demolished or converted to nonresidential use, and others may be split up into several
housing units. These addresses remain sample units,
resulting in a small variability in cluster size. Also, USUs
usually cover neighboring housing units, though, occasionally they are dispersed across a neighborhood, resulting in
a USU with housing units from different blocks.
Area frame. The area frame consists of housing units and
group quarters in census blocks that contain a high proportion of incomplete addresses, or are not covered by
building permit offices. A CPS USU in the area frame also
consists of about four housing unit equivalents, except in
some areas of Alaska that are difficult to access where a
USU is eight housing unit equivalents. The area frame is
converted into groups of four housing unit equivalents
called ‘‘measures’’ because the census addresses of individual housing units or persons within a group quarters are
not used in the sampling.
An integer number of area measures is calculated at the
census block level. The number is referred to as the area
block measure of size (MOS) and is calculated as follows:
H
area block MOS ⫽ 4 ⫹ 关GQ block MOS兴
共3.3兲
where
H
= the number of housing units enumerated in the block for the 1990 census.
GQ block MOS
= the integer number of group quarters
measures in a block (see equation
3.4).
The first term of equation (3.3) is rounded to the nearest
nonzero integer. When the fractional part is 0.5 and the
term is greater than 1, it is rounded to the nearest even
integer.
Sometimes census blocks are combined with geographically nearby blocks before the area block MOS is calculated. This is done to ensure that newly constructed units
3–9
have a chance of selection in blocks with no housing units
or group quarters at the time of the census and that are not
covered by a building permit office. This also reduces the
sampling variability caused by USU size differing from four
housing unit equivalents for small blocks with fewer than
four housing units.
Depending on whether or not a block is covered by a
building permit office, area frame blocks are classified as
area permit or area nonpermit. No distinction is made
between area permit and area nonpermit blocks during
sampling. Field procedures are developed to ensure proper
coverage of housing units built after the 1990 census in the
area blocks to (1) prevent these housing units from having
a chance of selection in area permit blocks and (2) give
these housing units a chance of selection in area nonpermit blocks. These field procedures have the added benefit
of assisting in keeping USU size constant as the number of
housing units in the block increases because of new
construction.
Group quarters frame. The group quarters frame consists
of group quarters in census blocks that contain a sufficient
proportion of complete addresses and are essentially covered by building permit offices. Although nearly all blocks
are covered by building permit offices, some are not, which
may result in minor undercoverage. The group quarters
frame covers a small proportion of the population. A CPS
USU in the group quarters frame consists of four housing
unit equivalents. The group quarters frame, like the area
frame, is converted into housing unit equivalents because
1990 census addresses of individual group quarters or
persons within a group quarters are not used in the
sampling. The number of housing unit equivalents is computed by dividing the 1990 census group quarters population by the average number of persons per household
(calculated from the 1990 census as 2.63).
An integer number of group quarters measures is calculated at the census block level. The number of group
quarters measures is referred to as the GQ block MOS and
is calculated as follows:
NIGQPOP
GQ block MOS ⫽ 共4兲共2.63兲 ⫹ MIL ⫹ IGQ
共3.4兲
where
NIGQPOP = the noninstitutional group quarters population in the block from the 1990 census.
MIL
= the number of military barracks in the
block from the 1990 census.
IGQ
= 1 if one or more institutional group quarters are in the block or 0 if no institutional
group quarters are in the block from the
1990 census.
The first term of equation (3.4) is rounded to the nearest
nonzero integer. When the fractional part is 0.5 and the
term is greater than 1, it is rounded to the nearest even
integer.
Only the civilian noninstitutional population is interviewed for CPS. Military barracks and institutional group
quarters are given a chance of selection in case group
quarters convert status over the decade. A military barrack
or institutional group quarters is equivalent to one measure
regardless of the number of people counted there in the
1990 census.
Special situations in old construction. During development of the old construction frames, several situations are
given special treatment. Military and national park blocks
are treated as if covered by a building permit office to
increase the likelihood of being in the unit or group quarters
frames to minimize costs. Blocks in American Indian Reservations are treated as if not covered by a building permit
office and are put in the area frame to improve coverage.
To improve coverage of newly constructed college housing,
special procedures are used so blocks with existing college
housing and small neighboring blocks are in the area
frame. Blocks in Ohio which are covered by building permit
offices that issue permits for only certain types of structures
are treated as area nonpermit blocks. Two examples of
blocks excluded from sampling frames are blocks consisting entirely of docked maritime vessels where crews reside
and street locations where only homeless people were
enumerated in the 1990 census.
The Permit Frame
Permit frame sampling ensures coverage of housing
units built since the 1990 census. The permit frame grows
as building permits are issued during the decade. Data
collected by the Building Permit Survey are used to update
the permit frame monthly. About 92 percent of the population lives in areas covered by building permit offices.
Housing units built since the 1990 census in areas of the
United States not covered by building permit offices have a
chance of selection in the nonpermit portion of the area
frame. Group quarters built since the 1990 census are
generally not covered in the permit frame, although the
area frame does pick up new group quarters. (This minor
undercoverage is discussed in Chapter 16.)
A permit measure which is equivalent to a CPS USU is
formed within a permit date and a building permit office
resulting in a cluster containing an expected four newly
built housing units. The integer number of permit measures
is referred to as the BPOMOS and is calculated as follows:
HPt
BPOMOSt ⫽ 4
共3.5兲
where
HPt = the total number of housing units for which the
building permit office issues permits for a time
period, t, normally a month; for example, a building permit office issued 2 permits for a total 24
housing units to be built in month t.
3–10
BPOMOS for time period t is rounded to the nearest integer
except when nonzero and less than 1, then it is rounded to
1. Permit cluster size varies according to the number of
housing units for which permits are actually issued. Also,
the number of housing units for which permits are issued
may differ from the number of housing units that actually
get built.
When developing the permit frame, an attempt is made
to ensure inclusion of all new housing units constructed
after the 1990 census. To do this, housing units for which
building permits had been issued but which had not yet
been constructed by the time of the census should be
included in the permit frame. However, by including permits
issued prior to the 1990 census in the permit frame, there
is a risk that some of these units will have been built by the
time of the census and, thus, included in the old construction frame. These units will then have two chances of
selection in the CPS: one in the permit frame and one in the
old construction frames.
For this reason, permits issued too long before the
census should not be included in the permit frame. However, excluding permits issued long before the census
brings the risk of excluding units for which permits were
issued but which had not yet been constructed by the time
of the census. Such units will have no chance of selection
in the CPS, since they are not included in either the permit
or old construction frames. In developing the permit frame,
an attempt is made to strike a reasonable balance between
these two problems.
Summary of Sampling Frames
Before providing a summary of the various sampling
frames, an exception is noted. Census blocks containing
sample selected by the National Health Interview Survey
(NHIS) were also included in the area frame to ensure a
housing unit was in sample for only one demographic
survey. That is, any sample (both housing unit and group
quarters) selected for the NHIS was transferred to the area
frame. Therefore, group quarters were in both the area and
group quarters frames. The NHIS had an all area frame
sample design because it was not conducted under Title
13; thus, it was prohibited from selecting a sample of 1990
census addresses.
Table 3–2 summarizes the features of the sampling
frames and CPS USU size discussed above. Roughly 65
percent of the CPS sample is from the unit frame, 30
percent is from the area frame, and 1 percent is from the
group quarters frame. In addition, about 5 percent of the
sample is from the permit frame initially. The permit frame
has grown, historically, about 1 percent a year. Optimal
cluster size or USU composition differs for the demographic surveys. The unit frame allows each survey a
choice of cluster size. For the area, group quarters, and
permit frames, MOS must be defined consistently for all
demographic surveys.
Table 3–2. Summary of Sampling Frames
Frame
Typical characteristics
of frame
CPS USU
Unit frame . . . . . . . . . . . High percentage of
Compact cluster of four
complete addresses in addresses
areas covered by a
building permit office
Group quarters frame . High percentage of
complete addresses in
areas covered by a
building permit office
Measure containing
group quarters of four
expected housing unit
equivalents
Area frame
Area permit . . . . . . . . Many incomplete
addresses in areas
covered by a building
permit office
Area nonpermit . . . . Not covered by a
building permit office
Measure containing
housing units and
group quarters of
four expected housing
unit equivalents
Permit frame. . . . . . . . . Housing units built
Cluster of four expected
since 1990 census in housing units
areas covered by a
building permit office
Selection of Sample Units
The CPS sample is designed to be self-weighting by
state or substate area. A systematic sample is selected
from each PSU at a sampling rate of 1 in k, where k is the
within-PSU sampling interval which is equal to the product
of the PSU probability of selection and the stratum sampling interval. The stratum sampling interval is usually the
overall state sampling interval. (See the earlier section in
this chapter, ‘‘Calculation of overall state sampling interval.’’)
The first stage of selection is conducted independently
for each demographic survey involved in the 1990 redesign. Sample PSUs overlap across surveys and have
different sampling intervals. To make sure housing units get
selected for only one survey, the largest common geographic areas obtained when intersecting each survey’s
sample PSUs are identified. These intersecting areas, as
well as the residual areas of those PSUs, are called basic
PSU components (BPCs). A CPS stratification PSU consists of one or more BPCs. For each survey, a within-PSU
sample is selected from each frame within BPCs. However,
sampling by BPCs is not an additional stage of selection.
After combining sample from all frames for all BPCs in a
PSU, the resulting within-PSU sample is representative of
the entire civilian, noninstitutional population of the PSU.
When CPS is not the first survey to select a sample in a
BPC, the CPS within-PSU sampling interval is decreased
to maintain the expected CPS sample size after other
surveys have removed sampled USUs. When a BPC does
not include enough sample to support all surveys present
in the BPC for the decade, each survey proportionally
reduces its expected sample size for the BPC. This makes
a state no longer self-weighting, but this adjustment is rare.
3–11
CPS sample is selected separately within each sampling
frame. Since sample is selected at a constant overall rate,
the percentage of sample selected from each frame is
proportional to population size. Although the procedure is
the same for all sampling frames, old construction sample
selection is performed once for the decade while permit
frame sample selection is an ongoing process each month
throughout the decade.
Within-PSU Sort
Units or measures are arranged within sampling frames
based on characteristics of the 1990 census and geography. Sorting minimizes within-PSU variance of estimates
by grouping together units or measures with similar characteristics. The 1990 census data and geography are used
to sort blocks and units. (Sorting is done within BPCs since
sampling is performed within BPCs.) The unit frame is
sorted on block level characteristics, keeping housing units
in each block together, and then by a housing unit identification to sort the housing units geographically. Sorts are
different for each frame and are provided in Tables 3–3a
and 3–3b.
General Sampling Procedure
The CPS sampling is a one-time operation that involves
selecting enough sample for the decade. To accommodate
the CPS rotation system and the phasing in of new sample
designs, 19 samples are selected. A systematic sample of
USUs is selected and 18 adjacent sample USUs identified.
The group of 19 sample USUs is known as a hit string. Due
to the sorting variables, persons residing in USUs within a
hit string are likely to have similar labor force characteristics.
The within-PSU sample selection is performed independently by BPC and frame. Four dependent random numbers (one per frame) between 0 and 1 are calculated for
each BPC within a PSU.9 Random numbers are used to
calculate random starts. Random starts determine the first
sampled USU in a BPC for each frame.
The method used to select systematic samples of hit
strings of USUs within each BPC and sampling frame
follows:
1. Units or measures within the census blocks are sorted
using the within-PSU sort criteria specified in Tables
3–3a and 3–3b.
2. Each successive USU not selected by another survey
is assigned an index number 1 through N.
3. A random start (RS) for the BPC/frame is calculated.
RS is the product of the dependent random number
and the adjusted within-PSU sampling interval (SIw).
4. Sampling sequence numbers are calculated. Given N
USUs, sequence numbers are:
RS, RS ⫹ 共1共SIw兲兲, RS ⫹ 共2共SIw兲兲, ..., RS ⫹ 共n共SIw兲兲
where n is the largest integer such that RS + (n(SIw)) ≤ N.
Sequence numbers are rounded up to the next integer.
Each rounded sequence number represents the first
unit or measure designating the beginning of a hit
string.
5. Sequence numbers are compared to the index numbers assigned to USUs. Hit strings are assigned to
sequence numbers. The USU with the index number
matching the sequence number is selected as the first
9
Random numbers are evenly distributed by frame within BPC and by
BPC within PSU to minimize variability of sample size.
Table 3–3a. Old Construction Within-PSU Sorts
Unit and area frames
Group quarters frame
Sort order
Urban PSUs
1 . . . . . . . . . . Block CBUR classification
1
Rural PSUs
Block CBUR classification
All PSUs
District Office
2 . . . . . . . . . . Proportion of minority renter occupied housing Proportion of households headed by females to Address Register Area
units to all occupied housing units in the block
all households in the block
3 . . . . . . . . . . Proportion of owner occupied housing units to Proportion of minority population to total popula- County code
total population age 16 years and over
tion in the block
4 . . . . . . . . . . Proportion of housing units to population age 16 Proportion of population age 65 and over to total MCD/CCD code
years and over in the block
population in the block
5 . . . . . . . . . . Proportion of households headed by females to Proportion of Black renter occupied housing units Block CBUR classification
all households in the block
to total Black population in the block
6 . . . . . . . . . . County code
County code
7 . . . . . . . . . . Tract number
Tract number
8 . . . . . . . . . . Combined block number (area frame only)
Combined block number (area frame only)
9 . . . . . . . . . . Housing unit ID (unit frame only)
Housing unit ID (unit frame only)
1
Block number
A census block is classified as C, B, U, or R, which means: central city of 1990 MSA (C); balance of 1990 urbanized area (B); other urban (U); or rural
(R).
3–12
Table 3–3b. Permit Frame Within-PSU Sort
Sort order
All PSUs
1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . County code
2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Building permit office
3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Permit date
sample. The 18 USUs that follow the sequence number are selected as the next 18 samples. This method
may yield hit strings with less than 19 samples (called
incomplete hit strings) at the beginning or end of
BPCs.10 Allowing incomplete hit strings ensures that
each USU has the same probability of selection.
6. A sample designation uniquely identifying 1 of the 19
samples is assigned to each USU in a hit string. For
the 1990 design, sample designations A62 through
A80 are assigned sequentially to the hit string. A62 is
assigned to the first sample; A63 to the second sample;
and assignment continues through A80 for the nineteenth sample. A sample designation suffix, A or B, is
assigned in areas of Alaska that are difficult to access
(in which USUs consist of eight housing unit equivalents).
Example of Within-PSU Sample Selection for
Old Construction
Although this example is for old construction, selecting a
systematic sample for the permit frame is similar except
that sampling is performed on an imaginary universe
(called a skeleton universe) consisting of an estimated
number of USUs within each BPC. As monthly permit
information becomes available, the skeleton universe gradually fills with issued permits and eventually specific sample
addresses are identified.
Table 3–4. Sampling Example Within a BPC for Any
of the Three Old Construction Frames
Census block
101 101A . . . . . . . . . . . . . . . . . . . . . .
103 104 . . . . . . . . . . . . . . . . . . . . . . .
106 . . . . . . . . . . . . . . . . . . . . . . . . . . .
107 107A . . . . . . . . . . . . . . . . . . . . . .
108 108D . . . . . . . . . . . . . . . . . . . . . .
The following example illustrates selection of withinPSU sample for an old construction frame within a BPC.
Assume blocks have been sorted within a BPC. The BPC
contains 18 unsampled USUs (N=18) and each USU is
assigned an index number (1, 2, ..., 18). The dependent
random number is 0.6528 and the SIw is 5.7604. To simplify
this example, four samples are selected and sample designations A1 through A4 assigned.
The random start is RS = 0.6528 x 5.7604 = 3.7604.
Sequence numbers are 3.7604, 9.5208, and 15.2812,
rounding up to 4, 10, and 16. These sequence numbers
represent first samples and correspond to index numbers
assigned to USUs. A hit string is assigned to each sequence
number to obtain the remaining three samples:
4, 10, 16 (first sample),
5, 11, 17 (second sample),
6, 12, 18 (third sample), and
7, 13, 111 (fourth sample).
This example includes an incomplete hit string at the
beginning and end of the BPC. After sample selection,
corresponding sample designations are assigned. Table
3–4 illustrates results from sample selection.
10
When RS + I > SIw, an incomplete hit string occurs at the beginning
of a BPC. When (RS + I) + (n( SIw)) > N, an incomplete hit string occurs
at the end of a BPC (I = 1 to 18).
11
Since 3.7604 + I > 5.7604 (RS + I > SIw) where I = 3, an incomplete
hit string occurs at the beginning of the BPC. The sequence number is
calculated as RS + I - SIw.
USU
number
within the
block
Index
number
Sample
designation
1
2
3
4
1
2
3
4
5
6
7
8
1
1
2
3
1
2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
A4
A1
A2
A3
A4
A1
A2
A3
A4
A1
A2
A3
Assignment of Post-Sampling Codes
Two types of post-sampling codes are assigned to the
sampled units. First, there are the CPS technical codes
used to weight the data, estimate the variance of characteristics, and identify representative subsamples of the
CPS sample units. The technical codes include final hit
number, rotation group, and random group codes. Second,
there are operational codes common to the demographic
household surveys used to identify and track the sample
units through data collection and processing. The operational codes include field PSU, segment number and
segment number suffix.
Final hit number. The final hit number identifies the original within-PSU order of selection. All USUs in a hit string
are assigned the same final hit number. For each PSU, this
code is assigned sequentially starting with one for both the
old construction and the permit frames. The final hit
number is used in the application of the CPS variance
estimation method discussed in Chapter 14.
Rotation group. Sample is partitioned into eight representative subsamples called rotation groups used in the CPS
rotation scheme. All USUs in a hit string are assigned to the
same rotation group. Assignment is performed separately
3–13
for old construction and the permit frame. Rotation groups
are assigned after sorting hits by state, MSA/non-MSA
status (old construction only), SR/NSR status, stratification
PSU, and final hit number. Because of this sorting, the
eight subsamples are balanced across stratification PSUs,
states, and the nation. Rotation group is used in conjunction with sample designation to determine units in sample
for particular months during the decade.
Random group. Sample is partitioned into ten representative subsamples called random groups. All USUs in the
hit string are assigned to the same random group. Assignment is performed separately for old construction and the
permit frame. Since random groups are assigned after
sorting hits by state, stratification PSU, rotation group, and
final hit number, the ten subsamples are balanced across
stratification PSUs, states, and the Nation. Random groups
can be used to partition the sample into test and control
panels for survey research.
Field PSU. A field PSU is usually a single county within a
stratification PSU, except in the New England states and
part of Hawaii where a field PSU is a group of minor civil
divisions. Field PSU definitions are consistent across all
demographic surveys and are more useful than stratification PSUs for coordinating field representative assignments among demographic surveys.
Segment number. A segment number is assigned to each
USU within a hit string. If a hit string consists of USUs from
only one field PSU, then the segment number applies to
the entire hit string. If a hit string consists of USUs in
different field PSUs, then each portion of the hit string/field
PSU combination gets a unique segment number. The
segment number is a four-digit code. The first digit corresponds to the rotation group of the hit. The remaining three
digits are sequence numbers. In any 1 month, a segment
within a field PSU identifies one USU or expected four
housing units that the field representative is scheduled to
visit. A field representative’s workload usually consists of a
set of segments within one or more adjacent field PSUs.
Segment number suffix. Adjacent USUs with the same
segment number may be in different blocks for area and
group quarters sample or in different building permit office
dates or ZIP Codes for permit sample, but in the same field
PSU. If so, an alphabetic suffix appended to the segment
number indicates that a hit string has crossed one of these
boundaries. Segment number suffixes are not assigned to
the unit sample.
Examples of Post-Sampling Code Assignments
Two examples are provided to illustrate assignment of
codes. To simplify the examples, only two samples are
selected, and sample designations A1 and A2 are assigned.
The examples illustrate a stratification PSU consisting of all
sampling frames (which often does not occur). Assume the
index numbers (shown in Table 3–5) are selected in two
BPCs.
These sample USUs are sorted and survey design
codes assigned as shown in Table 3–6.
The example in Table 3–6 illustrates that assignment of
rotation group and final hit number is done separately for
old construction and the permit frame. Consecutive numbers are assigned across BPCs within frames. Although
not shown in the example, assignment of consecutive
rotation group numbers (modulo 8) carries across stratification PSUs. For example, the first old construction hit in
the next stratification PSU is assigned to rotation group 1.
However, assignment of final hit numbers is performed
within stratification PSUs. A final hit number of 1 is assigned
to the first old construction hit and the first permit hit of each
stratification PSU. Operational codes are assigned as
shown in Table 3–7.
After sample USUs are selected and post-sampling
codes assigned, addresses are needed in order to interview sampled units. The procedure for obtaining addresses
differs by sampling frame. For operational purposes, identifiers are used in the unit frame during sampling instead of
actual addresses. The procedure for obtaining unit frame
addresses by matching identifiers to census files is described
in Chapter 4. Field procedures, usually involving a listing
operation, are used to identify addresses in other frames. A
description of listing procedures is also given in Chapter 4.
Illustrations of the materials used in the listing phase are
shown in Appendix B.
Table 3–5. Index Numbers Selected During Sampling for Code Assignment Examples
BPC number
1 .......................................................
2 .......................................................
Unit frame
3-4, 27-28, 51-52
10-11, 34-35
Group quarters
frame
Area frame
Permit frame
10-11 1 (incomplete), 32-33
none
6-7
7-8, 45-46
14-15
3–14
Table 3–6. Example of Postsampling Survey Design Code Assignments Within a PSU
Frame
Index
Sample
designation
Final hit number
Rotation group
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unit
Unit
Unit
Unit
Unit
Unit
Group quarters
Group quarters
Area
Area
Area
Unit
Unit
Unit
Unit
Area
Area
3
4
27
28
51
52
10
11
1
32
33
10
11
34
35
6
7
A1
A2
A1
A2
A1
A2
A1
A2
A2
A1
A2
A1
A2
A1
A2
A1
A2
1
1
2
2
3
3
4
4
5
6
6
7
7
8
8
9
9
8
8
1
1
2
2
3
3
4
5
5
6
6
7
7
8
8
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Permit
Permit
Permit
Permit
Permit
Permit
7
8
45
46
14
15
A1
A2
A1
A2
A1
A2
1
1
2
2
3
3
3
3
4
4
5
5
BPC
Table 3–7. Example of Postsampling Operational Code Assignments Within a PSU
BPC
County
Frame
Block
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1
1
2
2
2
1
1
1
1
1
3
3
3
3
3
3
Unit
Unit
Unit
Unit
Unit
Unit
Group quarters
Group quarters
Area
Area
Area
Unit
Unit
Unit
Unit
Area
Area
1
1
2
2
3
3
4
4
5
6
6
7
7
8
8
9
10
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1
2
2
3
3
Permit
Permit
Permit
Permit
Permit
Permit
THIRD STAGE OF THE SAMPLE DESIGN
Often, the actual USU size in the field can deviate from
what is expected from the computer sampling. Occasionally, the deviation is large enough to jeopardize the successful completion of a field representative’s assignment.
When these situations occur, a third stage of selection is
conducted to maintain a manageable field representative
workload. This third stage is called field subsampling.
Sample
Index designation
Final hit
number
Field PSU
Segment/
suffix
3
4
27
28
51
52
10
11
1
32
33
10
11
34
35
6
7
A1
A2
A1
A2
A1
A2
A1
A2
A2
A1
A2
A1
A2
A1
A2
A1
A2
1
1
2
2
3
3
4
4
5
6
6
7
7
8
8
9
9
1
1
1
2
2
2
1
1
1
1
1
3
3
3
3
3
3
8999
8999
1999
1999
2999
2999
3599
3599
4699
5699
5699
6999
6999
7999
7999
8699
8699A
7
8
45
46
14
15
A1
A2
A1
A2
A1
A2
1
1
2
2
3
3
1
1
2
2
3
3
3001
3001
4001
4001
5001
5001
Field subsampling occurs when a USU consists of more
than 15 sample housing units identified for interview.
Usually, this USU is identified after a listing operation. (See
Chapter 4 for a description of field listing.) The regional
office staff selects a systematic subsample of the USU to
reduce the number of sample housing units to a more
manageable number, from 8 to 15 housing units. To
facilitate the subsampling, an integer take-every (TE) and
start-with (SW) are used. An appropriate value of the TE
3–15
reduces the USU size to the desired range. For example, if
the USU consists of 16 to 30 housing units, a TE of 2
reduces USU size to 8 to 15 housing units. The SW is a
randomly selected integer between 1 and the TE.
Field subsampling changes the probability of selection
for the housing units in the USU. An appropriate adjustment to the probability of selection is made by applying a
special weighting factor in the weighting procedure. See
‘‘Special Weighting Adjustments’’ in Chapter 10.
ROTATION OF THE SAMPLE
The CPS sample rotation scheme is a compromise
between a permanent sample (from which a high response
rate would be difficult to maintain) and a completely new
sample each month (which results in more variable estimates of change). The CPS sample rotation scheme
represents an attempt to strike a balance in the minimization of the following:
1. Variance of estimates of month-to-month change: threefourths of the sample is the same in consecutive
months.
2. Variance of estimates of year-to-year change: one-half
of the sample is the same in the same month of
consecutive years.
3. Variance of other estimates of change: outgoing sample
is replaced by sample likely to have similar characteristics.
4. Response burden: eight interviews are dispersed across
16 months.
The rotation scheme follows a 4-8-4 pattern. A housing
unit or group quarters is interviewed 4 consecutive months,
not in sample for the next 8 months, interviewed the next 4
months, and then retired from sample. The rotation scheme
is designed so outgoing housing units are replaced by
housing units from the same hit string which have similar
characteristics.
The following summarizes the main characteristics (in
addition to the sample overlap described above) of the
CPS rotation scheme:
1. In any 1 month, one-eighth of the sample housing units
are interviewed for the first time; another eighth is
interviewed for the second time; and so on.
2. The sample for 1 month is composed of units from two
or three consecutive samples.
3. One new sample designation-rotation group is activated each month. The new rotation group replaces
the rotation group retiring permanently from sample.
4. One rotation group is reactivated each month after its
8-month resting period. The returning rotation group
replaces the rotation group beginning its 8-month
resting period.
5. Rotation groups are introduced in order of sample
designation and rotation group:
A62(1), A62(2), ..., A62(8), A63(1), A63(2), ..., A63(8),
..., A80(1), A80(2), ..., A80(8).
The present rotation scheme has been used since 1953.
The most recent research into alternate rotation patterns
was prior to the 1980 redesign when state-based designs
were introduced (Tegels, 1982).
The Rotation Chart
The CPS rotation chart illustrates the rotation pattern of
CPS sample over time. Figure 3–1 presents the rotation
chart beginning in January 1996. The following statements
provide guidance in interpreting the chart:
1. Numbers in the chart refer to rotation groups. Sample
designations appear in column headings. In January
1996, rotation groups 3, 4, 5, and 6 of A64; 7 and 8 of
A65; and 1 and 2 of A66 are designated for interview.
2. Consecutive monthly samples have six rotation groups
in common. The sample housing units in A64(4 - 6),
A65(8), and A66(1-2), for example, are interviewed in
January and February of 1996.
3. Monthly samples 1 year apart have four rotation
groups in common. For example, the sample housing
units in A65(7-8) and A66(1-2) are interviewed in
January 1996 and January 1997.
4. Of the two rotation groups replaced from month-tomonth, one is in sample for the first time and one
returns after being excluded for 8 months. For example,
in October 1996, the sample housing units in A67(3)
are interviewed for the first time and the sample
housing units in A65(7) are interviewed for the fifth
time after last being in sample in January.
Overlap of the Sample
Table 3–8 shows the proportion of overlap between any
2 months of sample depending on the time lag between
them. The proportion of sample in common has a strong
effect on correlation between estimates from different
months and, therefore, on variances of estimates of change.
3–16
Table 3–8. Proportion of Sample in Common
for 4-8-4 Rotation System
Figure 3–1. CPS Rotation Chart: January 1996 - April 1998
Sample designation and rotation groups
Interval (in months)
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4-8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16 and greater . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Percent of
sample in
common
between the
2 months
75
50
25
0
12.5
25
37.5
50
37.5
25
12.5
0
Phase-In of a New Design
When a newly redesigned sample is introduced
into the ongoing CPS rotation scheme, there are a
number of reasons not to discard the old CPS
sample one month and replace it with a completely
redesigned sample the next month. Since redesigned sample contains different sample areas, new
field representatives must be hired. Modifications in
survey procedures are usually made for a redesigned sample. These factors can cause discontinuity in estimates if the transition is made at one time.
Instead, a gradual transition from the old sample
design to the new sample design is undertaken.
Beginning in April 1994, the 1990 census-based
design was phased in through a series of changes
completed in July 1995 (U.S. Department of Labor,
1994).
Year/month
A64
1996 Jan
Feb
Mar
Apr
3456
4567
5678
678 1
May
June
July
Aug
Sept
Oct
Nov
Dec
1997 Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Dec
1998 Jan
Feb
Mar
Apr
A65
A66
A67
A68
A69
78 12
8 123
1234
2345
78 12
8 123
1234
2345
3456
4567
5678
678 1
3456
4567
5678
678 1
78 12
8 123
1234
2345
78 12
8 123
1234
2345
3456
4567
5678
678 1
3456
4567
5678
678 1
78 12
8 123
1234
2345
78 12
8 123
1234
2345
3456
4567
5678
678 1
3456
4567
5678
678 1
78 12
8 123
1234
2345
3–17
REFERENCES
Executive Office of the President, Office of Management
and Budget (1993), Metropolitan Area Changes Effective With the Office of Management and Budget’s
Bulletin 93-17, June 30, 1993.
Hansen, Morris H., Hurwitz, William N., and Madow,
William G. (1953), Survey Sample Methods and Theory,
Vol. I, Methods and Applications. New York: John Wiley
and Sons.
Hanson, Robert H. (1978), The Current Population
Survey: Design and Methodology, Technical Paper 40,
Washington, DC: Government Printing Office.
Kostanich, Donna, Judkins, David, Singh, Rajendra, and
Schautz, Mindi (1981), ‘‘Modification of Friedman-Rubin’s
Clustering Algorithm for Use in Stratified PPS Sampling,’’
paper presented at the 1981 Joint Statistical Meetings,
American Statistical Association.
Ludington, Paul W. (1992), ‘‘Stratification of Primary
Sampling Units for the Current Population Survey Using
Computer Intensive Methods,’’ paper presented at the
1992 Joint Statistical Meetings, American Statistical Association.
Statt, Ronald, Vacca, E. Ann, Wolters, Charles, and
Hernandez, Rosa (1981), ‘‘Problems Associated With Using
Building Permits as a Frame of Post-Census Construction:
Permit Lag and ED Identification,’’ paper presented at the
1981 Joint Statistical Meetings, American Statistical Association.
Tegels, Robert, and Cahoon, Lawrence (1982), ‘‘The
Redesign of the Current Population Survey: The Investigation Into Alternate Rotation Plans,’’ paper presented at the
1982 Joint Statistical Meetings, American Statistical Association.
U.S. Department of Labor, Bureau of Labor Statistics
(1994), ‘‘Redesign of the Sample for the Current Population
Survey,’’ Employment and Earnings, Washington, DC:
Government Printing Office, May 1994.
4–1
Chapter 4.
Preparation of the Sample
INTRODUCTION
The sample preparation operations have been developed to fulfill the following goals:
1. Implement the sampling procedures described in Chapter 3.
2. Produce virtually complete coverage of the eligible
population.
3. Ensure that only a trivial number of households will
appear in the sample more than once over the course
of a decade, or will appear in more than one of the
household surveys conducted by the Bureau of the
Census.
4. Provide cost efficient collection by producing most of
the sampling materials needed for both the Current
Population Survey (CPS) and other household surveys in a single, integrated operation.
The CPS is one of many household surveys conducted
on a regular basis by the Census Bureau. Insofar as
possible, Census Bureau programs have been designed
so that survey materials, survey procedures, personnel,
and facilities can be used by as many surveys as possible.
Sharing personnel and sampling material among a number
of programs yields obvious savings. For example, CPS
field representatives can be (and often are) employed on
non-CPS activities because the sampling materials, listing
and coverage instructions, and to a lesser extent, questionnaire content are similar for a number of different
programs. In addition, the sharing of sampling materials
makes it possible to keep respondents from being in more
than one sample.
The postsampling codes described in Chapter 3 identify,
among other information, the sample cases that are scheduled to be interviewed for the first time in each month of the
decade and indicate the types of materials (maps, listing of
addresses, etc.) needed by the census field representative
to locate the sample addresses. This chapter describes
how these materials are put together.
This chapter may occasionally indulge in detail beyond
the interest of the general reader. The next section is an
overview that is recommended for all readers. Subsequent
sections provide a more in-depth description of the CPS
sample preparation and will prove useful to those interested in greater detail.
Census Bureau headquarters are located in the Washington, DC area. Staff at headquarters coordinate CPS
functions ranging from sample design, sample selection,
and resolution of subject matter issues to administration of
the interviewing staffs maintained under the 12 regional
offices and data processing. Census Bureau staff located
in Jeffersonville, IN also participate in CPS planning and
administration. Their responsibilities include preparation
and dissemination of interviewing materials, such as maps
and segment folders. Nonetheless, the successful completion of the CPS data collection operations rests on the
combined efforts of all Census Bureau offices.
Monthly sample preparation of the CPS has three major
components:
1. Identifying addresses.
2. Listing living quarters.
3. Assigning sample to field representatives.
The within-PSU sample described in Chapter 3 is selected
from four distinct sampling frames, not all of which consist
of specific addresses. Since the field representatives need
to know the exact location of the households or group
quarters they are going to interview, much of sample
preparation involves the conversion of selected sample
(e.g., maps, lists of building permits) to a set of addresses.
This conversion is described below.
Address Identification in the Unit Frame
About 65 percent of the CPS sample is selected from the
unit frame. The unit frame sample is selected from a 1990
census file that contains the information necessary for
within-PSU sample selection, but does not contain address
information. The address information from the 1990 census
is stored in a separate file. The addresses of unit segments
are obtained by matching the file of 1990 census information to the file containing the associated 1990 census
addresses. This is a one-time operation for the entire unit
sample and is performed at headquarters. If the addresses
are thought to be incomplete (missing a house number or
street name), the 1990 census information is reviewed in
an attempt to complete the address before sending it to the
field representative for interview.
Address Identification in the Area Frame
About 30 percent of the CPS sample is selected from the
area frame. Measures of expected four housing units are
selected during within-PSU sampling instead of selecting
4– 2
housing units because the addresses are not city-style or
there is no building permit office coverage. This essentially
means that no particular housing units are as yet associated with the selected measure. The only information
available is a map of the block which contains the area
segment, the number of measures the block contains, and
which measure is associated with the area segment.
Before the individual housing units associated with the
area segment can be identified, additional procedures are
used to ensure that field representatives can locate the
housing units and that all newly built housing units have a
probability of selection. A field representative will be sent to
visit and canvass the block to create a complete list of the
housing units located in the block. This is referred to as a
listing operation which is described more thoroughly in the
next section. A systematic sampling pattern is applied to
this listing to identify the housing units in the area segment
that are designated for each month’s sample.
Systematic sampling pattern. Applying the systematic
sampling pattern to a listing is referred to as a Start-with/Takeevery procedure. Start-with/Take-every’s are calculated at
headquarters based on the first hit measure in the block
and on the number of measures in the block, respectively.
For more information on hits and blocks, see Chapter 3.
The regional offices use the Start-with/Take-every information to identify a sample housing unit to be interviewed. In
simplistic terms, the Start-with identifies the line number on
the listing sheet where the sampling pattern begins; the
Take-every identifies the frequency with which sample
housing units or lines on the listing sheet are selected.
To apply a Start-with/Take-every for a block, the regional
office would begin with the first listing sheet for the block
counting down the number of lines as indicated by the
Start-with. This resulting line number identifies a selected
sample housing unit. Beginning with the next line listed, the
regional office would count down the number of lines equal
to the Take-every. The resulting line identifies the next
selected sample housing unit. The regional office continues
the above approach until the sampling pattern has been
applied to all listed units. For example, a Start-with/Takeevery of 3/4 indicates that when the regional office applies
the sampling pattern, they would start on line three of the
listing sheet and take every fourth line. Suppose the listing
sheet had 12 listed lines, a Start-with/Take-every results in
the units on lines 3, 7, and 11 being selected for interview.
See Figure4-1.
Address Identification in the Group Quarters
Frame
About 1 percent of the CPS sample is selected from the
group quarters frame. The decennial census files did not
have information on the characteristics of the group quarters. The files contain information about the residents as of
April 1, 1990, but there was no information about their living
arrangements within the group quarters. Consequently, the
census information did not provide a tangible sampling unit
for the CPS. Measures were selected during within-PSU
sampling since there was no way to associate the selected
sample cases with persons to interview at a group quarters. A two-step process is used to identify the group
quarters segment. First, the group quarters addresses are
obtained by matching to the file of 1990 census addresses,
similar to the process for the unit frame. This is a one-time
operation done at headquarters. Before the persons living
at the group quarters associated with the group quarters
segment can be identified, an interviewer visits the group
quarters and creates a complete list of eligible sample units
(consisting of persons, rooms, or beds). This is referred to
as a listing operation. Then a systematic sampling pattern,
as described above, is applied to the listing to identify the
individuals at the group quarters in the group quarters
segment.
Address Identification in the Permit Frame
The proportion of the CPS sample selected from the
permit frame keeps increasing over the decade as new
housing units are constructed. The CPS sample redesigns
are introduced about 4 or 5 years after each decennial
census, and at this time the permit sample makes up about
5 percent of the CPS sample; this has historically increased
about 1 percent a year. Hypothetical measures are selected
during within-PSU sampling (skeleton sampling) in anticipation of the construction of new housing units. Identifying
the addresses for these permit measures involves a listing
operation at the building permit office, clustering of addresses
to form measures, and associating these addresses with
the hypothetical measures (or USUs) in sample.
The Census Bureau conducts the Building Permit Survey which collects information on a monthly basis from
each building permit office in the Nation about the number
of housing units authorized to be built. The Building Permit
Survey results are converted to measures of expected four
housing units. These measures are continuously accumulated and linked with the frame of hypothetical measures
used to select the CPS sample. This matching identifies
which building permit office contains the measure that is in
sample. A field representative then visits the building permit
office to obtain a list of addresses of units that were
authorized to be built; this is the Permit Address List (PAL)
operation. This list of addresses is keyed and transmitted to
headquarters where clusters are formed that correspond
one-to-one with the measures. Using this link, the clusters
of four addresses in each permit segment are identified.
Forming clusters. To help ensure some geographic clustering of addresses within permit measures and to make
PAL listing more efficient, information collected by the
4– 3
Figure 4– 1. Example of Systematic Sampling
4– 4
Survey of Construction (SOC)1 is used to identify many of
the addresses in the permit frame. For building permit
offices included in SOC, the Census Bureau collects
information on the characteristics of units to be built for
each permit issued by the building permit office. This
information is used to form measures in SOC building
permit offices. This is not the case for non-SOC building
permit offices.
1. SOC PALs are listings from building permit offices that
are in the SOC. If a building permit office is in the SOC,
then the actual permits issued by the building permit
office and the number of units authorized by each
permit (though not the addresses) are known in advance
of the match to the skeleton universe. Therefore, the
measures for sampling are formed directly from the
actual permits. The sample permits can then be identified. These sample permits are the only ones for
which addresses are collected.
Because measures for SOC permits were constrained to be within permits, once the listed addresses
are complete, the formation of clusters follows easily.
The measures formed at the time of sampling are, in
effect, the clusters. The sample measures within permits are fixed at the time of sampling; that is, there
cannot be any later rearrangement of these units into
more geographically compact clusters without voiding
the original sampling results.
Even without geographic clustering, there is some
degree of compactness inherent in the manner in
which measures are formed for sampling. The units
within the SOC permits are assigned to measures in
the same order in which they were listed for SOC; thus
units within apartment buildings will normally be in the
same clusters, and permits listed on adjacent lines on
the SOC listing often represent neighboring structures.
2. Non-SOC PALs are listings from building permit offices
not in the SOC. At the time of sampling, the only data
known for non-SOC building permit offices is a cumulative count of the units authorized on all permits for an
office for a given month (or year). Therefore, all
addresses for a building permit office/date are collected, together with the number of apartments in
multiunit buildings. The addresses are clustered using
all units on the PAL.
The purpose of clustering is to group units together
geographically, thus enabling a reduction in field travel
costs. For multiunit addresses, as many whole clusters
as possible are created from the units within each
address. The remaining units on the PAL are clustered
within ZIP Code and permit day of issue.
1
The Survey of Construction (SOC) is conducted by the U.S. Census
Bureau in conjunction with the Department of Housing and Urban
Development. It provides current regional statistics on starts and completions of new single-family and multifamily units and sales of new onefamily homes.
LISTING ACTIVITIES
When address information from the census is not available or the address information from the census no longer
corresponds to the current address situation, then a listing
of all eligible units must be created. Creating this list of
basic addresses is referred to as listing. Listing can occur
in all four frames: units within multiunit structures, living
quarters in blocks, units or residents within group quarters,
and addresses for building permits issued.
The living quarters to be listed are usually housing units.
In group quarters such as transient hotels, rooming houses,
dormitories, trailer camps, etc., where the occupants have
special living arrangements, the living quarters listed may
be housing units, rooms, beds, etc. In this discussion of
listing, all of these living quarters are included in the term
‘‘unit’’ when it is used in context of listing or interviewing.
Completed listings are sampled by regional office staff.
Performing the listing and sampling in two separate steps
allows each step to be verified and allows a more complete
control of sampling to avoid bias in designating the units to
be interviewed.
The units to be interviewed are selected as objectively
as possible to reduce bias. However, the sample rotation
system used in the CPS tends to remove biases that may
have been introduced in the selection process. For example,
if a unit USU is to be defined within a large structure where
units will be assigned to several USUs, the field representative could conceivably manipulate the order of listing of
the housing units to bias the selection for a given USU.
However, as the CPS rotation system eventually replaces
each USU by others made up of units adjacent on the
listing at the address, the unknown error from the selection
bias is replaced by a (measurable) variance.
In order to ensure accurate and complete coverage of
the area and group quarters segments, updating of the
initial listings is done periodically throughout the decade.
The updating ensures that changes such as units missed in
the initial listing, demolished units, residential/commercial
conversions, and new construction are accounted for.
Listing in the Unit Frame
Listing in the unit frame is usually not necessary. The
only time it is done is when the field representative
discovers that the address information from the 1990
census is no longer accurate for a multiunit structure and
the field representative cannot adequately reconcile the
differences.
For multiunit addresses (addresses where the expected
number or unit designations is two or more), the field
representative receives preprinted (computer-generated)
Unit/Permit Listing Sheets (Form 11-3) showing unit designations as recorded in the 1990 census. For multiunits
containing between two and nine units, the listing sheet
displays the unit designations of all units in the structure
4– 5
even if some of the units are not in sample. For multiunit
structures with ten or more units, the listing sheet contains
only the unit designations of sample cases. Other important information on the Unit/Permit Listing Sheet includes:
the address, the expected number of units at the address,
the sample designations, and serial numbers for the selected
sample units, and the 1990 census IDs corresponding to
those units. For an example of a Unit/Permit Listing Sheets
see Appendix B.
The first time a multiunit address enters the sample, the
field representative does one or more of the following:
• Verifies that the 1990 census information on the Unit/Permit
Listing Sheet (Form 11-3) is accurate.
• Corrects the listing sheet when it does not agree with
what the field representative finds at the address.
• Relists the address on a blank Unit/Permit Listing Sheet
(only for some large multiunit listings).
After the field representative has an accurate listing
sheet, he/she conducts an interview for each unit that has
a current (preprinted) sample designation. If an address is
relisted, the field representative provides information to the
regional office on the relisting. The regional office staff will
resample the listing sheet and provide the line numbers for
the specific lines on the listing sheet that identify the units
that should be interviewed.
A regular system of updating the listing in unit segments
is not followed; however, the field representative may
correct an in-sample listing during any visit if a change is
noticed. The change may result in additional units being
added or removed from sample.
For single-unit addresses, a preprinted listing sheet is
not provided to the field representative since only one unit
is expected based on the 1990 census information. If a field
representative discovers other units at the address at the
time of interview, these additional units are interviewed.
Listing in the Area Frame
All blocks that contain area frame sample cases must be
listed. Several months before the first area segment in a
block is to be interviewed, a field representative visits the
block to establish a list of living quarters. Units within the
block are recorded on an Area Segment Listing Sheet
(Form 11-5). The heading entries on the form are all filled
by a regional office clerk, and the field representative
records a systematic list of all occupied and vacant housing
units and group quarters within the block boundaries.
If the area segment is within a jurisdiction where building
permits are issued, housing units constructed since April 1,
1990, are eliminated from the area segment through the
year built procedure to avoid giving an address more than
one chance of selection. This is required because housing
units constructed since April 1, 1990, Census Day, are
represented by segments in the permit frame. When there
has been significant growth in housing units since April 1,
1990, it is necessary to determine the year built for every
structure in the segment at the time of listing. To determine
‘‘year built,’’ the field representative inquires at each listed
unit and circles the appropriate code on the listing sheet.
For an example of an Area Segment Listing Sheet see
Appendix B. The inquiry at the time of listing is omitted in
areas with low new construction activity; in such cases,
new construction units are identified later in completing the
coverage questions during the interview. This process
avoids an extra contact at units built before 1990.
If an area segment is not in a building permit issuing
jurisdiction, then housing units constructed after the 1990
census do not have a chance of being selected for interview in the permit frame. The field representative does not
determine ‘‘year built’’ for units in such blocks.
After the listing of living quarters in the area segment
has been completed, the listing forms are returned to the
regional office. A clerk in the regional office then applies the
sampling pattern to identify the units to be interviewed.
(See the Systematic sampling pattern section above.)
To reflect changes in the number of units in each sample
block, it is desirable to periodically review and correct (i.e.,
update) each segment’s listing so that the current USU,
and later USUs to be interviewed, will represent the current
status. The following rule is used: The USU being interviewed for the first time for CPS must be identified from a
listing that has been updated within the last 24 months.
The area listing sheet is updated by retracing the path of
travel of the original lister, verifying the existence of each
unit on the listing sheet, accounting for units no longer in
existence, and appending any unlisted housing units or
other living quarters that are found at the end of the list. By
extending the original sampling pattern, the appended
units are given their chance of being in sample.
Listing in the Group Quarters Frame
Group quarters addresses in the CPS sample are listed
if they are in the group quarters, area, or unit frames.
Group quarters found in the permit frame are not listed.
Before the first interviews at a group quarters address can
be conducted, a field representative visits the group quarters to establish a list of eligible units at the group quarters.
The same group quarters procedures for creating a list of
eligible units applies for group quarters found in the area or
unit frame. The only difference is the time frame in which
the listing is done. If the group quarters is in the group
quarters or area frame, then the listing is done several
months before interview. If the group quarters is discovered
at the address of unit frame sample, then the listing is done
at the time of interview.
Group Quarters Listing Sheets (Form 11-1) are used to
record the group quarters name, group quarters type,
address, the name and telephone number of a contact
person, and to list the eligible units within the group
4– 6
quarters. Institutional and verified military group quarters
are not listed by a field representative. For an example of a
Group Quarters Listing Sheet, see Appendix B.
Each eligible unit of a group quarters is listed on a
separate line of the group quarters listing sheet, regardless
of the number of units. If there is more than one group
quarters within the block, each group quarters must be
listed on a separate listing sheet. More detailed information
on the listing of group quarters can be found in the Listing
and Coverage Manual for Field Representatives. The rule
for the frequency of updating group quarters listings is the
same as for area segments.
Listing in the Permit Frame
There are two phases of listing in the permit frame. The
first is the PAL operation which establishes a list of
addresses authorized to be built by a building permit office.
This is done shortly after the permit has been issued by the
building permit office and associated with a sample hypothetical measure. The second listing is required when the
field representative visits the unit to conduct an interview
and discovers that the original address information collected through the PAL operation is not accurate and
cannot be reconciled.
PAL operation. For each building permit office containing
a sample measure, a PAL Form (Form 11-193A) is computergenerated. A field representative visits the building permit
office and completes the PALs by listing the necessary
permit and address information. If an address given on a
permit is missing a house number or street name (or
number), then the address is considered incomplete. In this
case, the field representative visits the new construction
site and draws a sketch map showing the location of the
structure and, if possible, completes the address.
Permit listing. Listing in the permit frame is necessary for
all multiunit addresses. The PAL operation does not obtain
unit designations at multiunit addresses. Therefore, listing
is necessary to complete the addresses. Additionally, listing
is required when the addresses obtained through the PAL
operation do not correspond to what the field representative actually finds when visiting the address for the first
interview. When this occurs, the unit frame listing procedures are followed.
THIRD STAGE OF THE SAMPLE DESIGN
(SUBSAMPLING)
Chapter 3 describes a third stage of the sample design.
This third stage is referred to as subsampling. The need for
subsampling is dependent on the results of the listing
operations and the results of the clerical sampling. Subsampling is required when the number of housing units in a
segment for a given sample is greater than 15 or when 1 of
the 4 units in the USU yields more than 4 total units. For
unit segments (and permit segments) this can happen
when more units than expected are found at the address at
the time of the first interview. For more information on
subsampling, see Chapter 3.
INTERVIEWER ASSIGNMENTS
The final stage of sample preparation includes those
operations that are needed in order to break the sample
down into manageable interviewer workloads and to get
the resulting assignments to the field representatives for
interview. At this point, all the sample cases for the month
have been identified and all the necessary information
about these sample cases is available in a central database at headquarters. The listings have been completed,
sampling patterns have been applied, and the addresses
are available. The central database also includes additional
information, such as telephone numbers for those cases
which have been interviewed in previous months. The
rotation of sample used in the CPS is such that seveneighths of the sample cases each month have been in
sample in previous months (see Chapter 3). This central
database is part of an integrated system described briefly
below. This integrated system also affects the management of the sample and the way the interview results are
transmitted to central headquarters, as described in Chapter 8.
Overview of the Integrated System
In recent years, technological advances have changed
the face of data preparation and collection activities at the
Census Bureau. The Census Bureau has been developing
computer-based methods for survey data collection, communications, management, and analysis. Within the Census Bureau, this integrated data collection system is called
the Computer Assisted Interviewing System. The computer
assisted interviewing system has two principal components: computer assisted personal interviewing (CAPI) and
centralized computer assisted telephone interviewing (CATI).
See Chapter 7 on the data collection aspects of this
system. The integrated system is designed to manage
decentralized data collection using laptop computers, a
centralized telephone collection, and a central database for
data management and accounting.
The integrated system is made up of three main parts:
1. Headquarters operations in the central database.
The headquarters operations include transmission of
cases to field representatives, transmission of CATI
cases to the telephone centers, and database maintenance.
2. Regional office operations in the central database.
The regional office operations include sample maintenance, keying of addresses as necessary, preparation
of assignments, determination of CATI assignments,
reinterview selection, and review and reassignment of
cases.
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3. Field representative case management operations
on the laptop computer. The field representative
operations include receipt of assignments, completion
of interview assignments, and transmittal of completed
work to the central database for processing (see
Chapter 8).
The central database resides at headquarters where the
file of sample cases is maintained. The database stores
field representative data (name, phone number, address,
etc.), information for making assignments (Field PSU, segment, address, etc.), and all the data for cases in sample.
Regional Office Operations
When the information in the database is complete, the
regional offices can begin the assignment preparation
phase. This includes making assignments to field representatives and sending cases to a centralized telephone
facility. Regional offices access the central database to
break down the assignment areas geographically and key
in information that is used to aid in the monthly field
representative assignment operations. The regional office
supervisor considers such characteristics as the size of the
Field PSU, the workload in that Field PSU, and the number
of field representatives working in that Field PSU when
deciding the best geographic method for dividing the
workload in Field PSUs among field representatives.
As part of the process of making field representative
assignments, the CATI assignments are also made. Each
regional office is informed of the recommended number of
cases that should be sent to CATI. The regional office
attempts to assign the recommended number of cases for
centralized telephone interviewing. The selection of cases
for CATI involves several steps. Cases may be assigned to
CATI if several criteria are met. These criteria pertain to the
Field PSU, the household, and the time in sample. In
general terms, the criteria are as follows:
1. The sample case must be in a Field PSU and random
group that is CATI eligible. (The random group restriction is imposed to allow for statistical testing. See
Chapter 3 for further description of random group.)
2. The household must have a telephone and be willing
to accept a telephone interview.
3. First and fifth month cases are generally not eligible for
a telephone or CATI interview.
The regional offices have the ability to temporarily
assign cases to CATI in order to cover their workloads in
certain situations. Each region has provided headquarters
with the number of CATI cases that they could provide to
CATI on a temporary basis each month. This is done
primarily to fill in for field representatives that are ill or on
vacation. When interviewing for the month is completed,
these cases will automatically be reassigned for CAPI
interviewing.
Many of the cases sent to CATI are successfully completed as telephone interviews. Those that cannot be
completed from the telephone centers are returned to the
field prior to the end of the interview period. These cases
are called ‘‘CATI recycles.’’ See Chapter 8 for further detail.
The final step is the certification of assignments. After all
changes to the interview and CATI assignments have been
made, the regional offices certify the assignments. This
must be done after all assignments have been reviewed for
geographic efficiency and have the proper balance among
field representatives.
After assignments are made and certified, the regional
offices transmit the assignments to the central database.
This transmission places the assignments on the telecommunications server for the field representatives and centralized telephone facilities. Prior to the interview period,
field representatives receive their assignments by initiating
a transmission to the telecommunications server at headquarters. Assignments include the instrument (questionnaire and/or supplements) and the cases they must interview that month. These files are copied to the laptop during
the transmission from the server. The instrument includes
the control card or household demographic information and
labor force questions. All data sent and received from the
field representatives pass through the central communications system maintained at headquarters. See Chapter 8
for more information on the transmission of interview
results.
Finally, the regional offices prepare the remaining paper
materials needed by the field representatives to complete
their assignments. The materials include:
1. Field Representative Assignment Listing (CAPI-35).
2. Segment folders for cases to be interviewed (including
maps, listing sheets, and other pertinent information
that will aid the interviewer in locating specific cases).
3. Blank listing sheets, payroll forms, respondent letters,
and other supplies requested by the field representative.
4. Segment folders and listing sheets for blocks and
group quarters to be listed for future CPS samples.
Once the field representative has received the above
materials and has successfully completed a transmission
to retrieve his/her assignments, the sample preparation
operations are complete and the field representative is
ready to conduct the interviews.
5–1
Chapter 5.
Questionnaire Concepts and Definitions for the Current
Population Survey
INTRODUCTION
An extremely important component of the Current Population Survey (CPS) is the questionnaire, also called the
survey instrument. Although the concepts and definitions of
both labor force items and associated demographic information collected in the CPS have, with a few exceptions,
remained relatively constant over the past several decades,
the survey instrument was radically redesigned in January
1994. The purpose of the redesign was to reflect changes
that have occurred in the economy and to take advantage
of the possibilities inherent in the automated data collection
methods introduced in 1994, that is, computer assisted
personal interviewing (CAPI) and computer assisted telephone interviewing (CATI). This chapter briefly describes
and discusses the current survey instrument: its concepts,
definitions, and data collection procedures and protocols.
STRUCTURE OF THE SURVEY INSTRUMENT
The CPS interview is divided into three basic parts: (1)
household and demographic information, (2) labor force
information, and (3) supplement information in months that
include supplements. Household and demographic information historically was called ‘‘control card information’’
because, in the paper-and-pencil environment, this information was collected on a separate cardboard form. With
electronic data collection, this distinction is no longer
apparent to the respondent or the interviewer. The order in
which interviewers attempt to collect information is as
follows: (1) housing unit data, (2) demographic data, (3)
labor force data, (4) more demographic data, (5) supplement data, and finally (6) more housing unit data.
Only the concepts and definitions of the household,
demographic, and labor force data are discussed below.
(For more information about supplements to the CPS see
Chapter 10.)
CONCEPTS AND DEFINITIONS
Household and Demographic Information
Upon contacting a household, interviewers proceed with
the interview unless there is a clear indication that the case
is a definite noninterview. (Chapter 7 discusses the interview process and explains refusals and other types of
noninterviews.) When interviewing a household for the first
time, interviewers collect information about the housing
unit and all individuals who usually live at the address.
Housing unit information. Upon first contact with a
housing unit, interviewers collect information on the housing unit’s physical address, its mailing address, the year it
was constructed, the type of structure (single or multiple
family), whether it is renter- or owner-occupied, whether
other units or persons are part of the sample unit, whether
the housing unit has a telephone and, if so, the telephone
number.
Household roster. After collecting or updating the housing
unit data, the interviewer either creates or updates a list of
all individuals living in the unit and determines whether or
not they are members of the household. This list is referred
to as the household roster.
Household respondent. One person may provide all of
the CPS data for the entire sample unit, provided that the
person is a household member 15 years of age or older
who is knowledgeable about the household. The person
who responds for the household is called the household
respondent. Information collected from the household respondent for other members of the household is referred to as
proxy response.
Reference person. To create the household roster, the
interviewer asks the household respondent to give ‘‘the
names of all persons living or staying’’ in the housing unit,
and to ‘‘start with the name of the person or one of the
persons who owns or rents’’ the unit. The person whose
name the interviewer enters on line one (presumably one
of the individuals who owns or rents the unit) becomes the
reference person. Note that the household respondent and
the reference person are not necessarily the same. For
example, if you are the household respondent and you give
your name ‘‘first’’ when asked to report the household
roster, then you also are the reference person. If, on the
other hand, you are the household respondent and you
give your spouse’s name ‘‘first’’ when asked to report the
household roster, then your spouse is the reference person. (Sometimes the reference person is referred to as the
‘‘householder.’’)
Household. A household is defined as all individuals
(related family members and all unrelated individuals)
whose usual place of residence at the time of the interview
5–2
is the sample unit. Individuals who are temporarily absent
and who have no other usual address are still classified as
household members even though they are not present in
the household during the survey week. College students
comprise the bulk of such absent household members, but
persons away on business or vacation are also included.
(Not included are persons in institutions or the military.)
Once household/nonhousehold membership has been established for all persons on the roster, the interviewer proceeds to collect all other demographic data for household
members only.
Relationship to reference person. The interviewer will
show a flash card with relationship categories (e.g., spouse,
child, grandchild, parent, brother/sister) to the household
respondent and ask him/her to report each household
member’s relationship to the reference person (the person
listed on line one). Relationship data also are used to
define families, subfamilies, and individuals whose usual
place of residence is elsewhere. A family is defined as a
group of two or more individuals residing together who are
related by birth, marriage, or adoption; all such individuals
are considered members of one family. Families are further
classified either as married-couple families or as families
maintained by women or men without spouses. Subfamilies are defined as families that live in housing units where
none of the members of the family are related to the
reference person. It is also possible that a household
contains unrelated individuals; that is, people who are not
living with any relatives. An unrelated individual may be
part of a household containing one or more families or
other unrelated individuals, may live alone, or may reside in
group quarters, such as a rooming house.
Additional demographic information. In addition to asking for relationship data, the interviewer asks for other
demographic data for each household member including:
birth date, marital status, who a particular person’s parent
is among the members of the household (entered as the
line number of parent) and who a person’s spouse is (if it
cannot be determined from the relationship data), Armed
Forces status, level of education, race, ethnicity, nativity,
and social security number (for those 15 years of age or
older in selected months). Total household income also is
collected. The social security number for each household
member 15 years of age or older is collected so that it is
possible to match the CPS data to other government data
in order to evaluate the accuracy, consistency, and comparability of various statistics derived from CPS data. The
following terms are used to define an individual’s marital
status at the time of the interview: married spouse present,
married spouse absent, widowed, divorced, separated, or
never married. The term ‘‘married spouse present’’ applies
to a husband and wife who both live at the same address,
even though one may be temporarily absent due to business, vacation, a visit away from home, a hospital stay, etc.
The term ‘‘married spouse absent’’ applies to individuals
who live apart for reasons such as marital problems, as
well as husbands and wives who are living apart because
one or the other is employed elsewhere, on duty with the
Armed Forces, or any other reason. The information collected during the interview is used to create three marital
status categories: single never married, married spouse
present, and other marital status. The latter category
includes those who were classified as widowed; divorced;
separated; or married, spouse absent.
Starting in January 1992, educational attainment for
each person in the household age 15 or older was obtained
through a question asking about the highest grade or
degree completed. In January 1996, additional questions
were added for several educational attainment categories
to ascertain the total number of years of school or credit
years completed. (Prior to January 1992, the questions
referred to the highest grade or year of school each person
had attended and, then, if they had completed that grade or
year of school.)
The household respondent identifies his/her own race
as well as that of other household members by selecting a
category from a flash card presented by the interviewer.
The categories on the flash card are: White, Black, American Indian, Aleut, Eskimo, Asian or Pacific Islander, or
Other. The ‘‘nativity’’ items ask about a person’s country of
birth, as well as that of the individual’s parents, and
whether they are American citizens and if so, whether by
birth or naturalization. Persons born outside of the 50
states also are asked date of immigration to the United
States. The nativity items were added in April of 1993.
The additional demographic data collected are used to
classify individuals into racial groups, education categories, and groups based on ethnic origin. Currently, the
racial categories for CPS data are White, Black, and Other,
where the Other group includes American Indians, Alaskan
Natives, and Asians and Pacific Islanders. The data about
individuals’ ethnic origin primarily are collected for the
purpose of identifying Hispanics,1 where Hispanics are
those who identify their origin or descent as Mexican,
Puerto Rican, Cuban, Central or South American, or some
other Hispanic descent. It should be noted that race and
ethnicity are distinct categories. Thus, individuals of Hispanic origin may be of any race.
Labor Force Information
Labor force information is obtained after the household
and demographic information has been collected. One of
the primary purposes of the labor force information is to
classify individuals as employed, unemployed, or not in the
labor force. Other information collected includes hours
worked, occupation, and industry and related aspects of
the working population. It should be noted that the major
labor force categories are defined hierarchically and, thus,
1
Hispanics may be of any race.
5–3
are mutually exclusive. Employed supersedes unemployed
which supersedes not in the labor force. For example,
individuals who are classified as employed, even if they
worked less than full time, are not asked the questions
about having looked for work, and hence cannot be classified as unemployed. Similarly, an individual who is classified as unemployed is not asked the questions used to
determine one’s primary nonlabor market activity. For
instance, retired persons who are currently working are
classified as employed even though they have retired from
previous jobs. Consequently, they are not asked the questions about their previous employment nor can they be
classified as retired. The current concepts and definitions
underlying the collection and estimate of the labor force
data are presented below.
Reference week. The CPS labor force questions ask
about labor market activities for 1 week each month. This
week is referred to as the ‘‘reference week.’’ The reference
week is defined as the 7-day period, Sunday through
Saturday, that includes the 12th of the month.
Civilian noninstitutional population. In the CPS, labor
force data are restricted to persons 16 years of age and
older, who currently reside in 1 of the 50 states or the
District of Columbia, who do not reside in institutions (e.g.,
penal and mental facilities, homes for the aged), and who
are not on active duty in the Armed Forces.
Employed persons. Employed persons are those who,
during the reference week (a) did any work at all (for at
least 1 hour) as paid employees; worked in their own
businesses, professions, or on their own farms; or worked
15 hours or more as unpaid workers in an enterprise
operated by a family member or (b) were not working, but
who had a job or business from which they were temporarily absent because of vacation, illness, bad weather,
childcare problems, maternity or paternity leave, labormanagement dispute, job training, or other family or personal reasons whether or not they were paid for the time off
or were seeking other jobs. Each employed person is
counted only once, even if he or she holds more than one
job. (See the discussion of multiple jobholders below.)
Employed citizens of foreign countries who are temporarily in the United States but not living on the premises of
an embassy are included. Excluded are persons whose
only activity consisted of work around their own house
(painting, repairing, cleaning, or other home-related housework) or volunteer work for religious, charitable, or other
organizations.
The initial survey question, asked only once for each
household, inquires whether anyone in the household has
a business or a farm. Subsequent questions are asked for
each household member to determine whether any of them
did any work for pay (or for profit if there is a household
business) during the reference week. If no work for pay or
profit was performed and a family business exists, respondents are asked whether they did any unpaid work in the
family business or farm.
Multiple jobholders. These are employed persons who,
during the reference week, had either two or more jobs as
wage and salary workers; were self-employed and also
held one or more wage and salary jobs; or worked as
unpaid family workers and also held one or more wage and
salary jobs. A person employed only in private households
(cleaner, gardener, babysitter, etc.) who worked for two or
more employers during the reference week is not counted
as a multiple jobholder since working for several employers
is considered an inherent characteristic of private household work. Also excluded are self-employed persons with
multiple unincorporated businesses and persons with multiple jobs as unpaid family workers.
Since January 1994, CPS respondents have been asked
questions each month to identify multiple jobholders. First,
all employed persons are asked ‘‘Last week, did you have
more than one job (or business, if one exists), including
part-time, evening, or weekend work?’’ Those who answer
‘‘yes’’ are then asked, ‘‘Altogether, how many jobs (or
businesses) did you have?’’ Prior to 1994, this information
had been available only through periodic CPS supplements.
Hours of work. Beginning with the CPS redesign in
January 1994, both actual and usual hours of work have
been collected. Prior to the redesign, only actual hours
were requested for all employed individuals.
Published data on hours of work relate to the actual
number of hours spent ‘‘at work’’ during the reference
week. For example, persons who normally work 40 hours a
week but were off on the Memorial Day holiday, would be
reported as working 32 hours, even though they were paid
for the holiday. For persons working in more than one job,
the published figures relate to the number of hours worked
at all jobs during the week.
Data on persons ‘‘at work’’ exclude employed persons
who were absent from their jobs during the entire reference
week for reasons such as vacation, illness, or industrial
dispute. Data also are available on usual hours worked by
all employed persons, including those who were absent
from their jobs during the reference week.
At work part time for economic reasons. Sometimes
referred to as involuntary part time, this category refers to
individuals who gave an economic reason for working 1 to
34 hours during the reference week. Economic reasons
include slack work or unfavorable business conditions,
inability to find full-time work, and seasonal declines in
demand. Those who usually work part time also must
indicate that they want and are available to work full time to
be classified as being part time for economic reasons.
At work part time for noneconomic reasons. This group
includes those persons who usually work part time and
were at work 1 to 34 hours during the reference week for a
5–4
noneconomic reason. Noneconomic reasons include illness or other medical limitation, childcare problems or
other family or personal obligations, school or training,
retirement or social security limits on earnings, and being in
a job where full-time work is less than 35 hours. The group
also includes those who gave an economic reason for
usually working 1 to 34 hours but said they do not want to
work full time or were unavailable for such work.
Usual full- or part-time status. In order to differentiate a
person’s normal schedule from his/her activity during the
reference week, persons also are classified according to
their usual full- or part-time statuses. In this context,
full-time workers are those who usually work 35 hours or
more (at all jobs combined). This group includes some
individuals who worked less than 35 hours in the reference
week — for either economic or noneconomic reasons — as
well as those who are temporarily absent from work.
Similarly, part-time workers are those who usually work
less than 35 hours per week (at all jobs), regardless of the
number of hours worked in the reference week. This may
include some individuals who actually worked more than
34 hours in the reference week, as well as those who were
temporarily absent from work. The full-time labor force
includes all employed persons who usually work full time
and unemployed persons who are either looking for fulltime work or are on layoff from full-time jobs. The part-time
labor force consists of employed persons who usually work
part time and unemployed persons who are seeking or are
on layoff from part-time jobs.
Prior to 1994, persons who worked full time during the
reference week were not asked about their usual hours.
Rather, it was assumed that they usually worked full time,
and hence they were classified as full-time workers.
Occupation, industry, and class-of-worker. For the employed,
this information applies to the job held in the reference
week. A person with two or more jobs is classified according to the job at which he or she worked the greatest
number of hours. The unemployed are classified according
to their last jobs. The occupational and industrial classification of CPS data is based on the coding systems used in
the 1990 census. A list of these codes can be found in
Alphabetical Index of Industries and Occupations (Bureau
of the Census, January 1992). The class-of-worker classification assigns workers to one of the following categories: wage and salary workers, self-employed workers, and
unpaid family workers. Wage and salary workers are those
who receive wages, salary, commissions, tips, or pay in
kind from a private employer or from a government unit.
The class-of-worker question also includes separate
response categories for ‘‘private for profit company’’ and
‘‘nonprofit organization’’ to further classify private wage and
salary workers (this distinction has been in place since
January 1994). Self-employed persons are those who work
for profit or fees in their own businesses, professions,
trades, or farms. Only the unincorporated self-employed
are included in the self-employed category since those
whose businesses are incorporated technically are wage
and salary workers because they are paid employees of a
corporation. Unpaid family workers are persons working
without pay for 15 hours a week or more on a farm or in a
business operated by a member of the household to whom
they are related by birth or marriage.
Occupation, industry, and class-of-worker on second
job. The occupation, industry, and class-of-worker information for individuals’ second jobs is collected in order to
obtain a more accurate measure of multiple jobholders, to
obtain more detailed information about their employment
characteristics, and to provide information necessary for
comparing estimates of number of employees in the CPS
and in BLS’s establishment survey (the Current Employment Statistics; for an explanation of this survey see BLS
Handbook of Methods, April 1997). For the majority of
multiple jobholders, occupation, industry, and class-ofworker data for their second jobs are collected only from a
quarter of the sample–those in their fourth or eighth monthly
interviews. However, for those classified as ‘‘self-employed
unincorporated’’ on their main jobs, class-of-worker of the
second job is collected each month. This is done because,
according to the official definition, individuals who are
self-employed unincorporated on both of their jobs are not
considered multiple jobholders.
The questions used to determine whether an individual is employed or not, along with the questions an
employed person typically will receive, are presented in
Figure 5–1. A copy of the entire questionnaire can be
obtained from the Internet. The address for this file is:
http://www.bls.census.gov/cps/bqestair.htm.
Earnings. Information on what people earn at their main
jobs is collected only for those who are receiving their
fourth or eighth monthly interviews. This means that earnings questions are asked of only one-fourth of the survey
respondents. Respondents are asked to report their usual
earnings before taxes and other deductions and to include
any overtime pay, commissions, or tips usually received.
The term ‘‘usual’’ is as perceived by the respondent. If the
respondent asks for a definition of usual, however, interviewers are instructed to define the term as more than half
the weeks worked during the past 4 or 5 months. Respondents may report earnings in the time period they prefer—for
example, hourly, weekly, biweekly, monthly, or annually.
(Allowing respondents to report in a periodicity with which
they were most comfortable was a feature added in the
1994 redesign.) Based on additional information collected
during the interview, earnings reported on a basis other
than weekly are converted to a weekly amount in later
processing. Data are collected for wage and salary workers
(excluding the self-employed who respond that their businesses were incorporated). These earnings data are used
to construct estimates of the distribution of usual weekly
earnings and median earnings. Individuals who do not
5–5
report their earnings on an hourly basis are asked if they
are, in fact, paid at an hourly rate and if so, what the hourly
rate is. The earnings of those who reported hourly and
those who are paid at an hourly rate is used to analyze the
characteristics of hourly workers, for example, those who
are paid the minimum wage.
Unemployed persons. All persons who were not employed
during the reference week but were available for work
(excluding temporary illness) and had made specific efforts
to find employment some time during the 4-week period
ending with the reference week are classified as unemployed. Individuals who were waiting to be recalled to a job
from which they had been laid off need not have been
looking for work to be classified as unemployed.
A relatively minor change was incorporated into the
definition of unemployment with the implementation of the
January 1994 redesign. Under the former definition, persons who volunteered that they were waiting to start a job
within 30 days (a very small group numerically) were
classified as unemployed, whether or not they were actively
looking for work. Under the new definition, by contrast,
people waiting to start a new job must have actively looked
for a job within the last 4 weeks in order to be counted as
unemployed. Otherwise, they are classified as not in the
labor force.
As the definition indicates, there are two ways people
may be classified as unemployed. They are either looking
for work (job seekers) or they have been temporarily
separated from a job (persons on layoff). Job seekers must
have engaged in active job search during the above
mentioned 4-week period in order to be classified as
unemployed. (Active methods are defined as job search
methods that have the potential to result in a job offer
without any further action on the part of the job seeker.)
Examples of active job search methods include going to an
employer directly or to a public or private employment
agency, seeking assistance from friends or relatives, placing or answering ads, or using some other active method.
Examples of the ‘‘other active’’ category include being on a
union or professional register, obtaining assistance from a
community organization, or waiting at a designated labor
pickup point. Passive methods, which do not qualify as job
search, include reading (as opposed to answering or
placing) ‘‘help wanted’’ ads and taking a job training course.
The response categories for active and passive methods
are clearly delineated in separately labeled columns on the
interviewers’ computer screens. Job search methods are
identified by the following questions: ‘‘Have you been doing
anything to find work during the last 4 weeks?’’ and ‘‘What
are all of the things you have done to find work during the
last 4 weeks?’’ To ensure that respondents report all of the
methods of job search used, interviewers ask ‘‘Anything
else?’’ after the initial or a subsequent job search method is
reported.
Persons ‘‘on layoff’’ are defined as those who have been
separated from a job to which they are waiting to be
recalled (i.e., their layoff status is temporary). In order to
measure layoffs accurately, the questionnaire determines
whether people reported to be on layoff did in fact have an
expectation of recall; that is, whether they had been given
a specific date to return to work or, at least, had been given
an indication that they would be recalled within the next 6
months. As previously mentioned, persons on layoff need
not be actively seeking work to be classified as unemployed.
Reason for unemployment. Unemployed individuals are
categorized according to their status at the time they
became unemployed. The categories are: (1) Job losers: a
group comprised of (a) persons on temporary layoff from a
job to which they expect to be recalled and (b) permanent
job losers, whose employment ended involuntarily and who
began looking for work; (2)Job leavers: persons who quit or
otherwise terminated their employment voluntarily and
began looking for work; (3)Persons who completed temporary jobs: persons who began looking for work after their
jobs ended; (4)Reentrants: persons who previously worked
but were out of the labor force prior to beginning their job
search; (5)New entrants: persons who never worked before
and who are entering the labor force for the first time. Each
of these five categories of unemployed can be expressed
as a proportion of the entire civilian labor force or as a
proportion of the total unemployed. Prior to 1994, new
entrants were defined as job seekers who had never
worked at a full-time job lasting 2 weeks or longer; reentrants were defined as job seekers who had held a full-time
job for at least 2 weeks and had then spent some time out
of the labor force prior to their most recent period of job
search. These definitions have been modified to encompass any type of job, not just a full-time job of at least 2
weeks duration. Thus, new entrants are now defined as job
seekers who have never worked at all, and reentrants are
job seekers who have worked before but not immediately
prior to their current job search.
Duration of unemployment. The duration of unemployment is expressed in weeks. For individuals who are
classified as unemployed because they are looking for
work, the duration of unemployment is the length of time
(through the current reference week) that they have been
looking for work. For persons on layoff, the duration of
unemployment is the number of full weeks (through the
reference week) they have been on layoff.
The questions used to classify an individual as unemployed can be found in Figure 5–1.
Not in the labor force. Included in this group are all
persons in the civilian noninstitutional population who are
neither employed nor unemployed. Information is collected
on their desire for and availability to take a job at the time
of the CPS interview, job search activity in the prior year,
and reason for not looking in the 4-week period prior to the
survey week. This group includes discouraged workers,
5–6
defined as persons not in the labor force who want and are
available for a job and who have looked for work sometime
in the past 12 months (or since the end of their last job if
they held one within the past 12 months), but are not
currently looking, because they believe there are no jobs
available or there are none for which they would qualify.
(Specifically, the main reason identified by discouraged
workers for not recently looking for work is one of the
following: Believes no work available in line of work or area;
could not find any work; lacks necessary schooling, training, skills, or experience; employers think too young or too
old; or other types of discrimination.)
Data on a larger group of persons outside the labor
force, one that includes discouraged workers as well as
persons who desire work but give other reasons for not
searching (such as childcare problems, family responsibilities, school, or transportation problems) are also published
regularly. This group is made up of persons who want a job,
are available for work, and have looked for work within the
past year. This group is generally described as having
some marginal attachment to the labor force.
Prior to January 1994, questions about the desire for
work among those who were not in the labor force were
asked only of a quarter of the sample. Since 1994, these
questions have been asked of the full CPS sample. Consequently since 1994, estimates of the number of discouraged workers as well as those with a marginal attachment
to the labor force are published monthly rather than just
quarterly.
Additional questions relating to individuals’ job histories
and whether they intend to seek work continue to be asked
only of persons not in the labor force who are in the sample
for either their fourth or eighth month. Data based on these
questions are tabulated only on a quarterly basis.
Estimates of the number of employed and unemployed
are used to construct a variety of measures. These measures include:
• Labor force: The labor force consists of all persons 16
years of age or older classified as employed or unemployed in accordance with the criteria described above.
• Unemployment rate: The unemployment rate represents the number of unemployed as a percentage of the
labor force.
• Labor force participation rate: The labor force participation rate is the proportion of the age-eligible population
that is in the labor force.
• Employment-population ratio: The employment-population
ratio represents the proportion of the age-eligible population that is employed.
Figure 5–1. Questions for Employed and
Unemployed
1. Does anyone in this household have a business or a
farm?
2. LAST WEEK, did you do ANY work for (either) pay (or
profit)?
Parenthetical filled in if there is a business or farm in
the household. If 1 is ‘‘yes’’ and 2 is ‘‘no,’’ ask 3. If 1
is ‘‘no’’ and 2 is ‘‘no,’’ ask 4.
3. LAST WEEK, did you do any unpaid work in the family
business or farm?
If 2 and 3 are both ‘‘no,’’ ask 4.
4. LAST WEEK, ( in addition to the business,) did you
have a job, either full or part time? Include any job from
which you were temporarily absent.
Parenthetical filled in if there is a business or farm in
the household.
If 4 is ‘‘no,’’ ask 5.
5. LAST WEEK, were you on layoff from a job?
If 5 is ‘‘yes,’’ ask 6. If 5 is ‘‘no,’’ ask 8.
6. Has your employer given you a date to return to work?
If ‘‘no,’’ ask 7.
7. Have you been given any indication that you will be
recalled to work within the next 6 months?
If ‘‘no,’’ ask 8.
8. Have you been doing anything to find work during the
last 4 weeks?
If ‘‘yes,’’ ask 9.
9. What are all of the things you have done to find work
during the last 4 weeks?
Individuals are classified as employed if they say ‘‘yes’’
to questions 2, 3 (and work 15 hours or more in the
reference week or receive profits from the business/farm),
or 4.
Individuals who are available to work are classified as
unemployed if they say ‘‘yes’’ to 5 and either 6 or 7, or if
they say ‘‘yes’’ to 8 and provide a job search method that
could have brought them into contact with a potential
employer in 9.
5–7
REFERENCES
U.S. Department of Commerce, U.S. Census Bureau
(1992),Alphabetical Index of Industries and Occupations, January 1992.
U.S. Department of Labor, Bureau of Labor Statistics
(1997), BLS Handbook of Methods, Bulletin 2490, April
1997.
6–1
Chapter 6.
Design of the Current Population Survey Instrument
INTRODUCTION
Chapter 5 describes the concepts and definitions underpinning the Current Population Survey (CPS) data collection instrument. The current survey instrument is the result
of an 8-year research and development effort to redesign
the data collection process and to implement previously
recommended changes in the underlying labor force concepts. The changes described here were introduced in
January 1994. Of particular interest in the redesign was the
wording of the labor force portion of the questionnaire and
the data collection methods. For virtually every labor force
concept the current questionnaire wording is different from
what was used previously. Data collection has also been
redesigned so that the instrument is fully automated and is
administered either on a laptop computer or from a centralized telephone facility.
MOTIVATION FOR THE REDESIGN OF THE
QUESTIONNAIRE COLLECTING OF LABOR
FORCE DATA
The CPS produces some of the most important data
used to develop economic and social policy in the United
States. Although the U.S. economy and society have
undergone major shifts in recent decades, the survey
questionnaire had remained unchanged since 1967. The
growth in the number of service-sector jobs and the decline
in the number of factory jobs have been two key developments. Other changes include the more prominent role of
women in the workforce and the growing popularity of
alternative work schedules. The 1994 revisions were designed
to accommodate these changes. At the same time, the
redesign took advantage of major advances in survey
research methods and data collection technology. Furthermore, recommendations for changes in the CPS had been
proposed in the late 1970s and 1980s, primarily by the
Presidentially-appointed National Commission on Employment and Unemployment Statistics (commonly referred to
as the Levitan Commission). No changes were implemented at that time, however, due to the lack of funding for
a large overlap sample necessary to assess the effect of
the redesign. In the mid-1980s, funding for an overlap
became available. Spurred by all of these developments, it
was decided to redesign the CPS questionnaire.
OBJECTIVES OF THE REDESIGN
There were five main objectives in redesigning the CPS
questionnaire: (1) to better operationalize existing definitions and reduce reliance on volunteered responses; (2) to
reduce the potential for response error in the questionnairerespondent-interviewer interaction and, hence, improve
measurement of CPS concepts; (3) to implement minor
definitional changes within the labor force classifications;
(4) to expand the labor force data available and improve
longitudinal measures; and (5) to exploit the capabilities of
computer assisted interviewing for improving data quality
and reducing respondent burden (see Copeland and Rothgeb (1990) for a fuller discussion).
Enhanced Accuracy
In redesigning the CPS questionnaire, the BLS and
Census Bureau attempted to develop questions that would
lessen the potential for response error. Among the approaches
used were: (1) shorter, clearer question wording; (2) splitting complex questions into two or more separate questions; (3) building concept definitions into question wording;
(4) reducing reliance on volunteered information; (5) explicit
and implicit strategies for the respondent to provide numeric
data on hours, earnings, etc.; and (6) the use of revised
precoded response categories for open-ended questions
(Copeland and Rothgeb, 1990.)
Definitional Changes
The labor force definitions used in the CPS have undergone only minor modifications since the survey’s inception
in 1940, and with only one exception, the definitional
changes and refinements made in 1994 were uniformly
small. The one major definitional change dealt with the
concept of discouraged workers; that is, persons outside
the labor force who are not looking for work because they
believed that there are no jobs available for them. As was
noted in Chapter 5, discouraged workers are similar to the
unemployed in that they are not working and want a job.
Since they are not conducting an active job search, however, they do not satisfy a key element necessary to be
classified as unemployed. The former measurement of
discouraged workers was criticized by the Levitan Commission as too arbitrary and subjective. It was deemed
arbitrary that assumptions about a person’s availability for
work were made from responses to a question on why the
respondent was not currently looking for work. It was
considered too subjective that the measurement was based
on a person’s stated desire for a job regardless of whether
the individual had ever looked for work. A new, more
6– 2
precise measurement of discouraged workers was introduced that specifically required that a person had searched
for a job during the prior 12 months and was available for
work. The new questions also enable estimation of the
number of persons outside the labor force who, although
they cannot be precisely defined as discouraged, satisfy
many of the same criteria as discouraged workers and thus
show some marginal attachment to the labor force.
Other minor changes were made to fine-tune the definitions of unemployment, categories of unemployed persons, and persons who were employed part time for
economic reasons.
New Labor Force Information Introduced
With the revised questionnaire, several types of labor
force data became available regularly for the first time. For
example, information is now available each month on
employed persons who have more than one job. Also, by
collecting information on the number of hours multiple
jobholders work on their main job and secondary jobs
separately, estimates of the number of workers who combined two or more part-time jobs into a full-time workweek,
and the number of full- and part-time jobs in the economy
can be made. The inclusion of the multiple jobholding
question also improves the accuracy of answers to the
questions on hours worked and facilitates comparisons of
employment estimates from the CPS with those from the
Current Employment Statistics program, the survey of
nonfarm business establishments (for a discussion of CES
survey see BLS Handbook of Methods, Bureau of Labor
Statistics, April 1997). In addition, beginning in 1994,
monthly data on the number of hours usually worked per
week and data on the number of discouraged workers are
collected from the entire CPS sample rather than from the
one-quarter sample of respondents in their fourth or eighth
monthly interviews.
Computer Technology
A key feature of the redesigned CPS is that the new
questionnaire was designed for a computer assisted interview. Prior to the redesign, CPS data were primarily
collected using a paper-and-pencil form. In an automated
environment, most interviewers now use laptop computers
on which the questionnaire has been programmed. This
mode of data collection is known as computer assisted
personal interviewing (CAPI). Interviewers ask the survey
questions as they appear on the screen of the laptop and
then type the responses directly into the computer. A
portion of sample households—currently about 18 percent—is
interviewed via computer assisted telephone interviewing
(CATI) from three centralized telephone centers located in
Hagerstown, MD; Tucson, AZ; and Jeffersonville, IN.
Automated data collection methods allow greater flexibility in questionnaire designing than paper-and-pencil
data collection methods. Complicated skips, respondentspecific question wording, and carry-over of data from one
interview to the next are all possible with an automated
environment. For example, automated data collection permits capabilities such as (1) the use of dependent interviewing, that is the carrying over of information from the
previous month — for industry, occupation, and duration of
unemployment data and (2) the use of respondent-specific
question wording based on the person’s name, age, and
sex, answers to prior questions, household characteristics,
etc. Furthermore, by automatically bringing up the next
question on the interviewer’s screen, computerization reduces
the probability of an interviewer asking the wrong set of
questions. The computerized questionnaire also permits
the inclusion of several built-in editing features, including
automatic checks for internal consistency, checking for
unlikely responses, and verification of answers. With these
built-in editing features, errors can be caught and corrected
during the interview itself.
Evaluation and Selection of Revised Questions
Planning for the revised CPS questionnaire began in
1986, when BLS and the Census Bureau convened a task
force to identify areas for improvement. Studies employing
methods from the cognitive sciences were conducted to
test possible solutions to the problems identified. These
studies included interviewer focus groups, respondent focus
groups, respondent debriefings, a test of interviewers’
knowledge of concepts, in-depth cognitive laboratory interviews, response categorization research, and a study of
respondents’ comprehension of alternative versions of
labor force questions (Campanelli, Martin, and Rothgeb,
1991; Edwards, Levine, and Cohany, 1989; Fracasso,
1989; Gaertner, Cantor, and Gay,1989; Martin, 1987; Palmisano, 1989).
In addition to the qualitative research mentioned above,
the revised questionnaire, developed jointly by Census
Bureau and BLS staff, used information collected in a large
two-phase test of question wording. During Phase I, two
alternative questionnaires were tested with the then official
questionnaire as the control. During Phase II, one alternative questionnaire was tested with the control. The questionnaires were tested using computer assisted telephone
interviewing and a random digit dialing sample (CATI/RDD).
During these tests, interviews were conducted from the
centralized telephone interviewing facilities of the Census
Bureau.
Both quantitative and qualitative information was used in
the two phases to select questions, identify problems, and
suggestion solutions. Analyses were based on information
from item response distributions, respondent and interviewer debriefing data, and behavior coding of interviewer/
respondent interactions.
Item Response Analysis
The primary use of item response analysis was to
determine whether different questionnaires produce different response patterns, which may, in turn, have affected
6– 3
the labor force estimates. Unedited data were used for this
analysis. Statistical tests were conducted to ascertain
whether response patterns for different questionnaire versions differed significantly. The statistical tests were adjusted
to take into consideration the use of a nonrandom clustered
sample, repeated measures over time, and multiple persons in a household.
Response distributions were analyzed for all items on
the questionnaires. The response distribution analysis indicated the degree to which new measurement processes
produced different patterns of responses. Data gathered
using the other methods outlined above also aided in the
interpretation of the response differences observed. (Response
distributions were calculated on the basis of persons who
responded to the item, excluding those for whom a ‘‘don’t
know’’ or ‘‘refused’’ was obtained.)
Respondent Debriefings
At the end of the interview, respondent debriefing questions were administered to a sample of respondents to
measure respondent comprehension and response formulation. Question-specific probes were used to ascertain
whether certain words, phrases, or concepts were understood by respondents in the manner intended. (Esposito et
al., 1992.). From these data, indicators of how respondents
interpret and answer the questions and some measures of
response accuracy were obtained.
The debriefing questions were tailored to the respondent
and depended on the path the interview had taken. Two
forms of respondent debriefing questions were administered—
probing questions and vignette classification. For example,
those who did not indicate that they had done any work in
the main survey were asked the direct probe ‘‘LAST WEEK
did you do any work at all, even for as little as 1 hour?’’ An
example of the vignettes respondents received is ‘‘Last
week, Amy spent 20 hours at home doing the accounting
for her husband’s business. She did not receive a paycheck.’’ Individuals were asked to classify the person in the
vignette as working or not working based on the wording of
the question they received in the main survey (e.g., ‘‘Would
you report her as working last week not counting work
around the house?’’ if the respondent received the unrevised questionnaire or ‘‘Would you report her as working for
pay or profit last week?’’ if the respondent received the
current, revised questionnaire (Martin and Polivka, 1995).
Behavior Coding
Behavior coding entails monitoring or audiotaping interviews, and recording significant interviewer and respondent behaviors (e.g., minor/major changes in question
wording, probing behavior, inadequate answers, requests
for clarification). During early stages of testing, behavior
coding data were useful in identifying problems with proposed questions. For example, if interviewers frequently
reword a question, this may indicate that the question was
too difficult to ask as worded; respondents’ requests for
clarification may indicate that they were experiencing comprehension difficulties; and interruptions by respondents
may indicate that a question was too lengthy (Esposito et
al., 1992).
During later stages of testing, the objective of behavior
coding was to determine whether the revised questionnaire
improved the quality of interviewer/respondent interactions
as measured by accurate reading of the questions and
adequate responses by respondents. Additionally, results
from behavior coding helped identify areas of the questionnaire that would benefit from enhancements to interviewer
training.
Interviewer Debriefings
The primary objective of interviewer debriefing was to
identify areas of the revised questionnaire or interviewer
procedures that were problematic for interviewers or respondents. The information collected during the debriefings was
useful in identifying which questions needed revision. The
information was also useful in modifying initial interviewer
training and the interviewer manual. A secondary objective
of interviewer debriefing was to obtain information about
the questionnaire, interviewer behavior, or respondent behavior that may help explain differences observed in the labor
force estimates from the different measurement processes.
Two different techniques were used to debrief interviewers. The first was the use of focus groups at the centralized
telephone interviewing facilities and in geographically dispersed regional offices. The focus groups were conducted
after interviewers had at least 3 to 4 months experience
using the revised CPS instrument. Approximately 8 to 10
interviewers were selected for each focus group. Interviewers were selected to represent different levels of experience and ability.
The second technique was the use of a self-administered
standardized interviewer debriefing questionnaire. Once
problematic areas of the revised questionnaire were identified through the focus groups, a standardized debriefing
questionnaire was developed and administered to all interviewers.
HIGHLIGHTS OF THE QUESTIONNAIRE
REVISION
A copy of the questionnaire can be obtained from
the Internet. The address for this file is:
http://www.bls.census.gov/cps/bqestair.htm
General
Definition of reference week. In the interviewer debriefings that were conducted in 13 different geographic areas
during 1988, interviewers reported that the current question 19 (Q19, major activity question) ‘‘What were you
6– 4
doing most of LAST WEEK, working or something else?’’
was unwieldy and sometimes misunderstood by respondents. In addition to not always understanding the intent of
the question, respondents were unsure what was meant by
the time period ‘‘last week’’ (BLS, 1988). A respondent
debriefing conducted in 1988 found that only 17 percent of
respondents had definitions of ‘‘last week’’ that matched
the CPS definition of Sunday through Saturday of the
reference week. The majority (54 percent) of respondents
defined ‘‘last week’’ as Monday through Friday (Campanelli
et al., 1991).
In the revised questionnaire, an introductory statement
was added with the reference period clearly stated. The
introductory statement reads as follows: ‘‘I am going to ask
a few questions about work-related activities LAST WEEK.
By last week I mean the week beginning on Sunday,
August 9 and ending Saturday, August 15.’’ This statement
makes the reference period more explicit to respondents.
Additionally, the former Q19 has been deleted from the
questionnaire. In the past, Q19 had served as a preamble
to the labor force questions, but in the revised questionnaire the survey content is defined in the introductory
statement, which also defines the reference week.
Direct question on presence of business. The definition
of employed persons includes those who work without pay
for at least 15 hours per week in a family business. In the
former questionnaire, there was no direct question on the
presence of a business in the household. Such a question
is included in the revised questionnaire. This question is
asked only once for the entire household prior to the labor
force questions. The question reads as follows: ‘‘Does
anyone in this household have a business or a farm?’’ With
this question it can be determined whether a business
exists and who in the household owns the business. The
primary purpose of this question is to screen for households that may have unpaid family workers, not to obtain an
estimate of household businesses. (See Rothgeb et al.
(1992), Copeland and Rothgeb (1990), and Martin (1987)
for a fuller discussion of the need for a direct question on
presence of a business.)
For households that have a family business, direct
questions are asked about unpaid work in the family
business of all persons who were not reported as working
last week. BLS produces monthly estimates of unpaid
family workers who work 15 or more hours per week.
Employment Related Revisions
Revised ‘‘At Work’’ question. Having a direct question on
the presence of a family business not only improved the
estimates of unpaid family workers, but also permitted a
revision of the ‘‘at work’’ question. In the former questionnaire, the ‘‘at work’’ question read: ‘‘LAST WEEK, did you
do any work at all, not counting work around the house?’’ In
the revised questionnaire, the wording reads, ‘‘LAST WEEK
did you do ANY work for (either) pay (or profit)?’’ (The
parentheticals in the question are read only when there is
a business or farm in the household.) The revised wording
‘‘work for pay (or profit)’’ better captures the concept of
work that BLS is attempting to measure. (See Martin
(1987) or Martin and Polivka (1995) for a fuller discussion
of problems with the concept of ‘‘work.’’)
Direct question on multiple jobholding. In the former
questionnaire, the actual hours question read: ‘‘How many
hours did you work last week at all jobs?’’ During the
interviewer debriefings conducted in 1988, it was reported
that respondents do not always hear the last phrase ‘‘at all
jobs.’’ Some respondents who work at two jobs may have
only reported hours for one job (BLS, 1988). In the revised
questionnaire, a question is included at the beginning of
the hours series to determine whether or not the person is
a multiple jobholder. A followup question also asks for the
number of jobs the multiple jobholder has. Multiple jobholders are asked about their hours on their main job and other
job(s) separately to avoid the problem of multiple jobholders not hearing the phrase ‘‘at all jobs.’’ These new
questions also allow monthly estimates of multiple jobholders to be produced.
Hours series. The old question on ‘‘hours worked’’ read:
‘‘How many hours did you work last week at all jobs?’’ If a
person reported 35-48 hours worked, additional followup
probes were asked to determine whether the person
worked any extra hours or took any time off. Interviewers
were instructed to correct the original report of actual
hours, if necessary, based on responses to the probes. The
hours data are important because they are used to determine the sizes of the full-time and part-time labor forces. It
is unknown whether respondents reported exact actual
hours, usual hours, or some approximation of actual hours.
In the revised questionnaire, a revised hours series was
adopted. An anchor-recall estimation strategy was used to
obtain a better measure of actual hours and to address the
issue of work schedules more completely. For multiple
jobholders, it also provides separate data on hours worked
at a main job and other jobs. The revised questionnaire first
asks about the number of hours a person usually works at
the job. Then, separate questions are asked to determine
whether a person worked extra hours, or fewer hours, and
finally a question is asked on the number of actual hours
worked last week. It also should be noted that the new
hours series allows monthly estimates of usual hours
worked to be produced for all employed persons. In the
former questionnaire, usual hours were obtained only in
the outgoing rotation for employed private wage and salary
workers and were available only on a quarterly basis.
Industry and occupation – dependent interviewing.
Prior to the revision, CPS industry and occupation (I&O)
data were not always consistent from month-to-month for
the same person in the same job. These inconsistencies
6– 5
arose, in part, because the household respondent frequently varies from 1 month to the next. Furthermore, it is
sometimes difficult for a respondent to describe an occupation consistently from month-to-month. Moreover, distinctions at the three-digit occupation and industry level, that is,
at the most detailed classification level, can be very subtle.
To obtain more consistent data and make full use of the
automated interviewing environment, dependent interviewing for the I&O questions was implemented in the revised
questionnaire for month-in-sample 2-4 households and
month-in-sample 6-8 households. Dependent interviewing
uses information collected during the previous month’s
interview in the current month’s interview. (Different variations of dependent interviewing were evaluated during
testing. See Rothgeb et al. (1991) for more detail.)
In the revised CPS, respondents are provided with the
name of their employer as of the previous month and asked
if they still work for that employer. If they answer ‘‘no,’’
respondents are asked the independent questions on
industry and occupation.
If they answer ‘‘yes,’’ respondents are asked ‘‘Have the
usual activities and duties of your job changed since last
month?’’ If individuals say ‘‘yes,’’ their duties have changed,
these individuals are then asked the independent questions on occupation, activities or duties, and class-ofworker. If their duties have not changed, individuals are
asked to verify the previous month’s description through
the question ‘‘Last month, you were reported as (previous
month’s occupation or kind of work performed) and your
usual activities were (previous month’s duties). Is this an
accurate description of your current job?’’
If they answer ‘‘yes,’’ the previous month’s occupation
and class-of-worker are brought forward and no coding is
required. If they answer ‘‘no,’’ persons are asked the
independent questions on occupation activities and duties
and class-of-worker. This redesign permits a direct inquiry
about job change before the previous month’s information
is provided to the respondent.
Earnings. The earnings series in the revised questionnaire is considerably different from that in the former
questionnaire. In the former questionnaire, persons were
asked whether they were paid by the hour, and if so, what
the hourly wage was. All wage and salary workers were
then asked for their usual weekly earnings. In the former
version, earnings could be reported as weekly figures only,
even though that may not have been the easiest way for
the respondent to recall and report earnings. Data from
early tests indicated that a small proportion (14 percent)
(n=853) of nonhourly wage workers were paid at a weekly
rate and less than 25 percent (n=1623) of nonhourly wage
workers found it easiest to report earnings as a weekly
amount.
In the revised questionnaire, the earnings series is
designed to first request the periodicity for which the
respondent finds it easiest to report earnings and then
request an earnings amount in the specified periodicity, as
displayed below. The wording of questions requesting an
earnings amount is tailored to the periodicity identified
earlier by the respondent. (Because data on weekly earnings are published quarterly by BLS, earnings data provided by respondents in periodicities other than weekly are
converted to a weekly earnings estimate later during
processing operations.)
Revised Earnings Series (Selected items)
1. For your (MAIN) job, what is the easiest way for you to
report your total earnings BEFORE taxes or other
deductions: hourly, weekly, annually, or on some other
basis?
2. Do you usually receive overtime pay, tips, or commissions (at your MAIN job)?
3. (Including overtime pay, tips and commissions,) What
are your usual (weekly, monthly, annual, etc.) earnings
on this job, before taxes or other deductions?
As can be seen from the revised questions presented
above, other revisions to the earnings series include a
specific question to determine whether a person usually
receives overtime pay, tips, or commissions. If so, a
preamble precedes the earnings questions that reminds
respondents to include overtime pay, tips, and commissions when reporting earnings. If a respondent reports that
it is easiest to report earnings on an hourly basis, then a
separate question is asked regarding the amount of overtime pay, tips and commissions usually received, if applicable.
An additional question is asked of persons who do not
report that it is easiest to report their earnings hourly. The
question determines whether they are paid at an hourly
rate and is displayed below. This information, which allows
studies of the effect of the minimum wage, is used to
identify hourly wage workers.
‘‘Even though you told me it is easier to report your
earnings annually, are you PAID AT AN HOURLY RATE
on this job?’’
Unemployment Related Revisions
Persons on layoff - direct question. Previous research
(Rothgeb, 1982; Palmisano, 1989) demonstrated that the
former question on layoff status—‘‘Did you have a job or
business from which you were temporarily absent or on
layoff LAST WEEK?’’—was long, awkwardly worded, and
frequently misunderstood by respondents. Some respondents heard only part of the question, while others thought
that they were being asked whether they had a business.
In an effort to reduce response error, the revised questionnaire includes two separate direct questions about
layoff and temporary absences. The layoff question is:
‘‘LAST WEEK, were you on layoff from a job?’’ Questions
asked later screen out those persons who do not meet the
criteria for layoff status.
6– 6
Persons on layoff— expectation of recall. The official
definition of layoff includes the criterion of an expectation of
being recalled to the job. In the former questionnaire,
persons reported to be on layoff were never directly asked
whether they expected to be recalled. In an effort to better
capture the existing definition, persons reported to be on
layoff in the revised questionnaire are asked ‘‘Has your
employer given you a date to return to work?’’ Persons who
respond that their employers have not given them a date to
return are asked ‘‘Have you been given any indication that
you will be recalled to work within the next 6 months?’’ If the
response is positive, their availability is determined by the
question, ‘‘Could you have returned to work LAST WEEK if
you had been recalled?’’ Persons who do not meet the
criteria for layoff are asked the job search questions so they
still have an opportunity to be classified as unemployed.
Job search methods. The concept of unemployment
requires, among other criteria, an active job search during
the past 4 weeks. In the former questionnaire, the following
question was asked to determine whether a person conducted an active job search. ‘‘What has ... been doing in the
last 4 weeks to find work?’’ checked with—
• public employment agency
• private employment agency
• employer directly
• friends and relatives
• placed or answered ads
• nothing
• other
Interviewers were instructed to code all passive job
search methods into the ‘‘nothing’’ category. This included
such activities as looking at newspaper ads, attending job
training courses, and practicing typing. Only active job
search methods for which no appropriate response category exists were to be coded as ‘‘other.’’
In the revised questionnaire, several additional response
categories were added and the response options reordered and reformatted to more clearly represent the
distinction between active job search methods and passive
methods. The revisions to the job search methods question
grew out of concern that interviewers were confused by the
precoded response categories. This was evident even
before the analysis of the CATI/RDD test. Martin (1987)
conducted an examination of verbatim entries for the
‘‘other’’ category and found that many of the ‘‘other’’
responses should have been included in the ‘‘nothing’’
category instead. The analysis also revealed responses
coded as ‘‘other’’ that were too vague to determine whether
or not an active job search method had been undertaken.
Fracasso (1989) also concluded that the current set of
response categories was not adequate for accurate classification of active and passive job search methods.
During development of the revised questionnaire, two
additional passive categories were included: (1)‘‘looked at
ads’’ and (2) ‘‘attended job training programs/courses,’’ and
two additional active categories were included: (1)‘‘contacted school/university employment center’’ and (2)‘‘checked
union/ professional registers.’’ Later research also demonstrated that interviewers had difficulty coding relatively
common responses such as ‘‘sent out resumes’’ and ‘‘went
on interviews’’; thus the response categories were further
expanded to reflect these common job search methods.
Duration of job search and layoff. The duration of
unemployment is an important labor market indicator published monthly by BLS. In the former questionnaire, this
information was collected by the question: ‘‘How many
weeks have you been looking for work?’’ This wording
forced people to report in a periodicity that may not have
been meaningful to them, especially for the longer-term
unemployed. Also, asking for the number of weeks (rather
than months) may have led respondents to underestimate
the duration. In the revised questionnaire, the question
reads: ‘‘As of the end of LAST WEEK, how long had you
been looking for work?’’ Respondents can select the periodicity themselves and interviewers are able to record the
duration in weeks, months, or years.
To avoid clustering of answers around whole months,
the revised questionnaire also asks persons who report
duration in whole months (between 1 and 4 months) a
followup question to obtain an estimated duration in weeks:
‘‘We would like to have that in weeks, if possible. Exactly
how many weeks had you been looking for work?’’ The
purpose of this is to lead people to report the exact number
of weeks instead of multiplying their monthly estimates by
four as was done in an earlier test and may have been
done in the former questionnaire.
As mentioned earlier, the CATI/CAPI technology makes
it possible to automatically update duration of job search
and layoff for persons who are unemployed in consecutive
months. For persons reported to be looking for work for 2
consecutive months or longer, the previous month’s duration is updated without re-asking the duration questions.
For persons on layoff for at least 2 consecutive months, the
duration of layoff is also automatically updated. This revision was made to reduce respondent burden and enhance
the longitudinal capability of the CPS. This revision also will
produce more consistent month-to-month estimates of
duration. Previous research indicates that only about 25
percent of those unemployed in consecutive months who
received the former questionnaire (where duration was
collected independently each month) increased their reported
durations by 4 weeks plus or minus a week. (Polivka and
Rothgeb, 1993; Polivka and Miller, 1995). A very small bias
is introduced when a person has a brief (less than 3 or 4
6– 7
weeks) period of employment in between surveys. However, testing revealed that only 3.2 percent of those who
had been looking for work in consecutive months said that
they had worked in the interlude between the surveys.
Furthermore, of those who had worked, none indicated that
they had worked for 2 weeks or more.
Revisions Included in ‘‘Not in the Labor Force’’
Related Questions
Response options of retired, disabled, and unable to
work at key labor force items. In the former questionnaire, when individuals reported they were retired in response
to any of the labor force items, the interviewer was required
to continue asking whether they worked last week, were
absent from a job, were looking for work, and, in the
outgoing rotation, when they last worked and their job
histories. Interviewers commented that elderly respondents frequently complained that they had to respond to
questions that seemed to have no relevance to their own
situations.
In an attempt to reduce respondent burden, a response
category of ‘‘retired’’ was added to each of the key labor
force status questions in the revised questionnaire. If
individuals 50 years of age or older volunteer that they are
retired, they are immediately asked a question inquiring
whether they want a job. If they indicate that they want to
work, they are then asked questions about looking for work
and the interview proceeds as usual. If they do not want to
work, the interview is concluded and they are classified as
not in the labor force - retired. (If they are in the outgoing
rotation, an additional question is asked to determine
whether they worked within the last 12 months. If so, the
industry and occupation questions are asked about the last
job held.)
A similar change has been made in the revised questionnaire to reduce the burden for individuals reported to be
‘‘unable to work’’ or ‘‘disabled.’’ (Individuals who may be
‘‘unable to work’’ for a temporary period of time may not
consider themselves as ‘‘disabled’’ so both response options
are provided.) If a person is reported to be ‘‘disabled’’ or
‘‘unable to work’’ at any of the key labor force classification
items, a followup question is asked to determine whether
he/she can do any gainful work during the next 6 months.
Different versions of the followup probe are used depending on whether the person is disabled or unable to work.
Dependent interviewing for persons reported to be
retired, disabled, or unable to work. The revised questionnaire also is designed to use dependent interviewing
for persons reported to be retired, disabled, or unable to
work. An automated questionnaire increases the ease with
which information from the previous month’s interview can
be used during the current month’s interview.
Once it is reported that the person did not work during
the current month’s reference week, the previous month’s
status of retired (if a person is 50 years of age or older),
disabled, or unable to work is verified, and the regular
series of labor force questions is not asked. This revision
reduces respondent and interviewer burden.
Discouraged workers. The implementation of the Levitan
Commission’s recommendations on discouraged workers
resulted in one of the major definitional changes in the
1994 redesign. The Levitan Commission criticized the
former definition because it was based on a subjective
desire for work and questionable inferences about an
individual’s availability to take a job. As a result of the
redesign, two requirements were added: For persons to
qualify as discouraged, they must have engaged in some
job search within the past year (or since they last worked if
they worked within the past year), and they must currently
be available to take a job. (Formerly, availability was
inferred from responses to other questions; now there is a
direct question.)
Data on a larger group of persons outside the labor force
(one that includes discouraged workers as well as persons
who desire work, but give other reasons for not searching,
such as child care problems, family responsibilities, school,
or transportation problems) also are published regularly.
This group is made up of persons who want a job, are
available for work, and have looked for work within the past
year. This group is generally described as having some
marginal attachment to the labor force. Also beginning in
1994, questions on this subject are asked of the full CPS
sample rather than limited to a quarter of the sample,
permitting estimates of the number of discouraged workers
to be published monthly rather than quarterly.
From data available during the tests of the revised
questionnaire, it is clear that improvements in data quality
for labor force classification have been obtained as a result
of the redesign of the CPS questionnaire, and in general,
measurement error has been reduced. Data from respondent debriefings, interviewer debriefings, and response
analysis demonstrated that the revised questions are more
clearly understood by respondents and the potential for
labor force misclassification is reduced. Results from these
tests formed the basis for the design of the final revised
version of the questionnaire. This revised version was
tested in a separate year and a half parallel survey prior to
implementation as the official survey in January 1994. In
addition, from January 1994 through May 1994, the unrevised procedures were used with the parallel survey sample.
These parallel surveys were conducted to assess the effect
of the redesign on national labor force estimates. Estimates derived from the initial year and a half of the parallel
survey indicated that the redesign might increase the
unemployment rate by 0.5 percentage points. However,
subsequent analysis using the entire parallel survey indicates that the redesign did not have a statistically significant effect on the unemployment rate. (Analysis of the
effect of the redesign on the unemployment rate and other
labor force estimates can be found in Cohany, Polivka, and
6– 8
Rothgeb (1994). Analysis of the redesign on the unemployment rate along with a wide variety of other labor force
estimates using data from the entire parallel survey can be
found in Polivka and Miller (1995).)
CONTINUOUS TESTING AND IMPROVEMENTS
OF THE CURRENT POPULATION SURVEY AND
ITS SUPPLEMENTS
Experience gained during the redesign of the CPS has
demonstrated the importance of testing questions and
monitoring data quality. The experience, along with contemporaneous advances in research on questionnaire design,
also has helped inform the development of methods for
testing new or improved questions for the basic CPS and
its periodic supplements (Martin, 1987; Oksenberg, Bischoping, K., Cannell and Kalton, 1991; Campanelli, Martin,
and Rothgeb, 1991; Esposito et al., 1992; and Forsyth and
Lessler, 1991). Methods to continuously test questions and
assess data quality are discussed in Chapter 15. It is
important to note, however, that despite the benefits of
adding new questions and improving existing ones, changes
in the CPS should be approached cautiously and the
effects measured and evaluated. When possible, methods
to bridge differences caused by changes or techniques to
avoid the disruption of historical series should be included
in the testing of new or revised questions.
REFERENCES
Bischoping, K. (1989), An Evaluation of Interviewer
Debriefings in Survey Pretests, In C. Cannell et al. (eds.),
New Techniques for Pretesting Survey Questions, Chapter 2.
Campanelli, P.C., Martin, E.A., and Rothgeb, J.M. (1991),
The Use of Interviewer Debriefing Studies as a Way to
Study Response Error in Survey Data, The Statistician,
vol. 40, pp. 253-264.
Cohany, S.R., Polivka, A.E., and Rothgeb, J.M., (1994)
Revisions in the Current Population Survey Effective January 1994, Employment and Earnings, February 1994 vol.
41 no. 2 pp. 1337.
Copeland, K. and Rothgeb, J.M. (1990), Testing Alternative Questionnaires for the Current Population Survey,
Proceedings of the Section on Survey Research Methods, American Statistical Association, pp. 63-71.
Edwards. S.W., Levine, R., and Cohany, S.R., (1989),
Procedures for Validating Reports of Hours Worked and for
Classifying Discrepancies Between Reports and Validation
Totals, Proceedings of the Section on Survey Research
Methods, American Statistical Association.
Esposito, J.L., Rothgeb,J.M., Polivka, A.E., Hess, J.,
and Campanelli, P.C. (1992), Methodologies for Evaluating
Survey Questions: Some Lessons From the Redesign of
the Current Population Survey, Paper presented at the
International Conference on Social Science Methodology,
Trento, Italy, June, 1992.
Forsyth, B.H. and Lessler, J.T. (1991), Cognitive Laboratory Methods: A Taxonomy, in P.P Biemer, R.M. Groves,
L.E. Lyberg, N.A. Mathiowetz, and S. Sudman (eds.),
Measurement Errors in Surveys, New York: Wiley, pp.
393-418.
Fracasso, M.P. (1989), Categorization of Responses to
the Open-Ended Labor Force Questions in the Current
Population Survey, Proceedings of the Section on Survey Research Methods, American Statistical Association,
pp. 481-485.
Gaertner, G., Cantor, D., and Gay, N. (1989), Tests of
Alternative Questions for Measuring Industry and Occupation in the CPS, Proceedings of the Section on Survey
Research Methods, American Statistical Association.
Martin, E.A., (1987), Some Conceptual Problems in the
Current Population Survey, Proceedings of the Section
on Survey Research Methods, American Statistical Association, pp. 420-424.
Martin, E.A. and Polivka, A.E. (1995), Diagnostics for
Redesigning Survey Questionnaires: Measuring Work in
the Current Population Survey, Public Opinion Quarterly,
Winter 1995 vol. 59, no.4, pp. 547-67.
Oksenberg, L., Cannell, C., and Kalton, G. (1991), New
Strategies for Pretesting Survey Questions, Journal of
Official Statistics, vol. 7, no. 3, pp. 349-365.
Palmisano, M. (1989), Respondents Understanding of
Key Labor Force Concepts Used in the CPS, Proceedings
of the Section on Survey Research Methods, American
Statistical Association.
Polivka, A.E. and Miller, S.M. (1998),The CPS After the
Redesign Refocusing the Economic Lens, Labor Statistics Measurement Issues, edited by J. Haltiwanger et al.,
pp. 249-86.
Polivka, A.E. and Rothgeb, J.M. (1993), Overhauling the
Current Population Survey: Redesigning the Questionnaire, Monthly Labor Review, September 1993 vol. 116,
no. 9, pp. 10-28.
Rothgeb, J.M. (1982), Summary Report of July Followup
of the Unemployed, Internal memorandum, December 20,
1982.
Rothgeb, J.M., Polivka, A.E., Creighton, K.P., and Cohany,
S.R. (1992), Development of the Proposed Revised Current Population Survey Questionnaire, Proceedings of
the Section on Survey Research Methods, American
Statistical Association.
U.S. Department of Labor, Bureau of Labor Statistics
(1988), Response Errors on Labor Force Questions Based
on Consultations With Current Population Survey Interviewers in the United States. Paper prepared for the OECD
Working Party on Employment and Unemployment Statistics.
U.S. Department of Labor, Bureau of Labor Statistics
(1997), BLS Handbook of Methods, Bulletin 2490, April
1997.
7–1
Chapter 7.
Conducting the Interviews
INTRODUCTION
Each month during interview week, field representatives
(FRs) and computer assisted telephone interviewers (CATI)
attempt to contact and interview a responsible person living
in each sample unit to complete a Current Population
Survey (CPS) interview. Typically, the week containing the
19th of the month is the interview week. The week containing the 12th is the reference week (i.e., the week about
which the labor force questions are asked). In December,
the week containing the 12th is used as interview week,
provided the reference week (in this case the week containing the 5th) falls entirely within the month of December.
As outlined in Chapter 3, households are in sample for 8
months. Each month, one-eighth of the households are in
sample for the first time (month-in-sample 1 (MIS-1)),
one-eighth for the second time, etc. Because of this,
different types of interviews (due to differing MIS) are
conducted by each FR within his/her assignment. An
introductory letter is sent to each household in sample prior
to its first and fifth month interviews. The letter describes
the CPS, announces the forthcoming visit, and provides
respondents with information regarding their rights under
the Privacy Act, the voluntary nature of the survey, and the
guarantees of confidentiality for the information they provide. Figure 7–1 shows the introductory letter sent to
sample units in the area administered by the Chicago
Regional Office. A personal visit interview is required for all
first month-in-sample households. This is due to the fact
that the CPS sample is strictly a sample of addresses. The
Census Bureau has no way of knowing who the occupants
of the sample household are, much less whether the
household is occupied or eligible for interview. (Note: In
some MIS-1 households, telephone interviews are conducted. This occurs when, during the initial personal contact, the respondent requests a telephone interview.)
NONINTERVIEWS AND HOUSEHOLD
ELIGIBILITY
The FR’s first task is to establish the eligibility of the
sample address for CPS. There are many reasons an
address may not be eligible for interview. The address may
have been converted to a permanent business, condemned
or demolished, or falls outside the boundaries of the
segment for which it was selected. Regardless of the
reason, such sample addresses are classified as Type C
noninterviews. The Type C units have no chance of becoming eligible for the CPS interview in future months, because
the condition is considered permanent. These addresses
are stricken from the roster of sample addresses and are
never visited again with regard to CPS. All households
classified as Type C undergo a full supervisory review of
the circumstances surrounding the case before such a
determination is made final.
Other sample addresses are also ineligible for CPS
interview. These are units which are intended for occupancy but are not occupied by any eligible individuals.
Reasons for such ineligibility include a vacant housing unit
(either for sale or rent), occupied entirely by individuals
who are not eligible for a CPS labor force interview
(individuals with a usual residence elsewhere (URE), in the
Armed Forces, or children under the age of 15), or other
reasons why a housing unit is temporarily not occupied.
Such units are classified as Type B noninterviews. These
noninterviews are considered unavoidable. Type B noninterview units have a chance of becoming eligible for
interview in future months, because the condition is considered temporary (e.g., a vacant unit could become occupied). Therefore, Type B units are reassigned to FRs in
subsequent months. These sample addresses remain in
sample for the entire 8 months that households are eligible
for interview. Each succeeding month, an FR visits the unit
to determine whether the unit has changed status, and
either continues the Type B classification, revises the
noninterview classification, or conducts an interview as
applicable. Some of these Type B households are found to
be eligible for the Housing Vacancy Survey (HVS). See
Chapter 11 for a description of the HVS.
Additionally, one final set of households is not interviewed for CPS; these are called Type A households.
These are households that the FR has determined as
eligible for a CPS interview, but for which no useable data
were collected. To be eligible the unit has to be occupied by
at least one person eligible for an interview (an individual
who is a civilian, at least 15 years old, and does not have
a usual residence elsewhere). Even though such households are eligible for interview, they are not interviewed
because the household members refuse, are absent during
the interviewing period, or are unavailable for other reasons. All Type A cases are subject to full supervisory review
before such a determination is made final. Every effort is
made to keep such noninterviews to a minimum. All Type A
cases remain in sample and are assigned for interview in
all succeeding months. Even in cases of confirmed refusals
7– 2
Figure 7– 1. Introductory Letter
7– 3
(cases that still refuse to be interviewed despite supervisory attempts to convert the case), the FR must verify that
the same household still resides at that address before
submitting a Type A noninterview.
Figure 7– 2 shows how the three types of noninterviews
are classified and the various reasons for reaching such
results. Even if a unit is designated as a noninterview, FRs
are responsible for collecting information about the unit.
Figure 7– 3 lists the main housing unit items that are
collected for noninterviews and summarizes each item
briefly.
Figure 7– 2. Noninterviews: Types A, B, and C
Note: See the CPS Interviewing Manual for more details
regarding the answer categories under each type of noninterview. See figure 7– 3 for a list of the main housing unit
items asked for noninterview and a brief description of what
each item asks.
TYPE A
1
2
3
4
No one home
Temporarily absent
Refusal
Other occupied
TYPE B
1
2
3
4
5
6
7
8
9
Vacant regular
Temporarily occupied by persons with usual residence
elsewhere
Vacant -- storage of household furniture
Unfit or to be demolished
Under construction, not ready
Converted to temporary business or storage
Unoccupied tent site or trailer site
Permit granted, construction not started
Other Type B -- specify
TYPE C
1
2
3
4
5
6
8
9
Demolished
House or trailer moved
Outside segment
Converted to permanent business or storage
Merged
Condemned
Unused line of listing sheet
Other -- specify
INITIAL INTERVIEW
If the unit is not classified as a noninterview, the FR
initiates the CPS interview. The FR attempts to interview a
knowledgeable adult household member (known as the
household respondent). The FRs are trained to ask the
questions exactly as worded as they appear on the computer screen. The interview begins with the verification of
the unit’s address and confirmation of its eligibility for a
CPS interview. Part 1 of Figure 7– 4 shows the household
items asked at the beginning of the interview. Once this is
established, the interview moves into the demographic
portion of the instrument. The primary task of this portion of
the interview is to establish the household’s roster (the
listing of all household residents at the time of the interview). At this point in the interview, the main concern is to
establish an individual’s usual place of residence. (These
rules are summarized in Figure 7– 5.) For all individuals
residing in the household without a usual residence elsewhere, a number of personal and family demographic
characteristics are collected. Part 1 of Figure 7– 6 shows
the demographic items asked in MIS-1 households.
These characteristics are the relationship to the reference person (the person who owns or rents the home),
parent or spouse pointers (if applicable), age, sex, marital
status, educational attainment, veteran’s status, current
Armed Forces status, race, and origin. As discussed in
Figure 7– 7, these characteristics are collected in an interactive format that includes a number of consistency edits
embedded in the interview itself. The goal is to collect as
consistent a set of demographic characteristics as possible. The final steps in this portion of the interview are to
verify the accuracy of the roster. To this end, a series of
questions is asked to ensure that all household members
have been accounted for. Before moving on to the labor
force portion of the interview, the FR is prompted to review
the roster and all data collected up to this point. A chance
is provided for the FR to correct any incorrect or inconsistent information at this time. The instrument then begins the
labor force portion of the interview.
In a household’s initial interview, a few additional characteristics are collected after completion of the labor force
portion of the interview. This information includes a question on family income and data on all household members’
countries of birth (along with the country of birth of his/her
father and mother) and, for the foreign born, data on year
of entry into the United States and citizenship status. See
Part 2 of Figure 7– 6 for a list of these items.
After completing the household roster, the FR collects
the labor force data described in Chapter 6. The labor force
data are collected from all civilian adult individuals (age 15
and older) who do not have a usual residence elsewhere.
To the greatest extent possible, the FR attempts to collect
this information from each eligible individual him/herself. In
the interest of timeliness and efficiency, however, a household respondent (any knowledgeable adult household member) generally is used to collect the data. Just over one-half
of the CPS labor force data are collected by self-response.
The bulk of the remainder is collected by proxy from the
household respondent. Additionally, in certain limited situations, collection of the data from a nonhousehold member
is allowed. All such cases receive direct supervisory review
before the data are accepted into the CPS processing
system.
7– 4
Figure 7– 3. Noninterviews: Main Housing Unit Items Asked for Types A, B, and C
Note: This list of items is not all inclusive. The list covers only the main data items and does not include related items used
to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the
actual instrument screens for all CPS items.
Housing Unit Items for Type A Cases
Item Name
Item Asks
1
2
3
4
5
TYPEA
ABMAIL
PROPER
ACCES-scr
LIVQRT
6
INOTES-1
Which specific kind of Type A is the case.
What is the property’s mailing address.
If there is any other building in the property (occupied or vacant).
If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.
What type of housing unit is it (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based on
observation.
If the interviewer wants to make any notes about the case that might help with the next interview.
Housing Unit Items for Type B Cases
Item Name
Item Asks
1
2
3
4
5
6
7
TYPEB
ABMAIL
BUILD
FLOOR
PROPER
ACCES-scr
LIVQRT
8
9
SEASON
BCINFO
10
INOTES-1
Which specific kind of Type B is the case.
What is the property’s mailing address.
If there are any other units (occupied or vacant) in the unit.
If there are any occupied or vacant living quarters besides this one on this floor.
If there is any other building in the property (occupied or vacant).
If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.
What is the type of housing unit (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based
on observation.
If the unit is intended for occupancy year round, by migratory workers, or seasonally.
What are the name, title, and phone number of contact who provided Type B or C information; or if the information was
obtained by interviewer observation.
If the interviewer wants to make any notes about the case that might help with the next interview.
Housing Unit Items for Type C Cases
Item Name
Item Asks
1
2
3
4
TYPEC
PROPER
ACCES-scr
LIVQRT
5
BCINFO
6
INOTES-1
Which specific kind of Type C is the case.
If there is any other building in the property (occupied or vacant).
If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.
What type of housing unit is it (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based on
observation.
What are the name, title, and phone number of contact who provided Type B or C information; or if the information was
obtained by interviewer observation.
If the interviewer wants to make any notes about the case that might help with the next interview.
SUBSEQUENT MONTHS’ INTERVIEWS
For households in sample for the second, third, and
fourth months, the FR has the option of conducting the
interview over the telephone. Use of this interviewing mode
must be approved by the respondent. Such approval is
obtained at the end of the first month’s interview upon
completion of the labor force and supplemental, if any,
questions. This is the preferred method for collecting the
data; it is much more time and cost efficient. We obtain
approximately 85 percent of interviews in these 3 monthsin-samples (MIS) via the telephone. See Part 2 of Figure
7– 4 for the questions asked to determine household eligibility and obtain consent for the telephone interview. A
personal visit interview attempt is required for the fifthmonth interview. After this initial attempt, a telephone
interview may be conducted provided the original household still occupies the sample unit. This is after a sample
unit’s 8-month dormant period and is used to reestablish
rapport with the household. Fifth-month households are
more likely than any other MIS household to be a replacement household. A replacement household is one in which
all the previous month’s residents have moved out and
been replaced by an entirely different group of residents.
This can and does occur in any MIS except for MIS 1
households. As with their MIS 2, 3, and 4 counterparts,
households in their sixth, seventh, and eighth MIS are
eligible for telephoning interviewing. Once again we collect
about 85 percent of these cases via the telephone.
The first thing the FR does in subsequent interviews is
update the household roster. The instrument presents a
screen (or a series of screens for MIS 5 interviews) that
7– 5
Figure 7– 4. Interviews: Main Housing Unit Items Asked in MIS 1 and Replacement Households
Note: This list of items is not all inclusive. The list covers only the main data items and does not include related items used
to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the
actual instrument screens for all CPS items.
Part 1. Items Asked at the Beginning of the Interview
Item Name
Item Asks
1
2
3
4
5
6
7
8
9
10
INTRO-b
NONTYP
VERADD
MAILAD
STRBLT
BUILD
FLOOR
PROPER
TENUR-scrn
ACCES-scr
11
12
MERGUA
LIVQRT
13
14
LIVEAT
HHLIV
If interviewer wants to classify case as a noninterview.
What type of noninterview the case is (A, B, or C); asked depending on answer to INTRO-b.
What is the street address (as verification).
What is the mailing address (as verification).
If the structure was originally built before or after 4/1/90.
If there are any other units (occupied or vacant) in the building.
If there are any occupied or vacant living quarters besides the sample unit on the same floor.
If there is any other building in the property (occupied or vacant).
If unit is owned, rented, or occupied without paid rent.
If access to household is direct or through another unit; this item is answered by the interviewer (not read to the
respondent).
If the sample unit has merged with another unit.
What type of housing unit is it (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer (not read
to the respondent).
If all persons in the household live or eat together.
If any other household on the property lives or eats with the interviewed household.
Part 2. Items Asked at the End of the Interview
Item Name
Item Asks
15
16
TELHH-scrn
TELAV-scrn
17
18
19
20
21
22
23
TELWHR-scr
TELIN-scrn
TELPHN
BSTTM-scrn
NOSUN-scrn
THANKYOU
INOTES-1
If there is a telephone in the unit.
If there is a telephone elsewhere on which people in this household can be contacted; asked depending on answer to
TELHH-scrn.
If there is a telephone elsewhere, where is the phone located; asked depending on answer to TELAV-scrn.
If a telephone interview is acceptable.
What is the phone number and whether it is a home or office phone.
When is the best time to contact the respondent.
If a Sunday interview is acceptable.
If there is any reason why the interviewer will not be able to interview the household next month.
If the interviewer wants to make any notes about the case that might help with the next interview; also asks for a list of
names/ages of ALL additional persons if there are more than 16 household members.
verifies the accuracy of the roster. Since households in MIS
5 are returning to sample after an 8-month hiatus, additional probing questions are asked to correctly establish
the household’s current roster and update some characteristics. See Figure 7– 8 for a list of major items asked in
MIS 5 interviews. If there are any changes, the instrument
goes through the steps necessary to add or delete an
individual(s). Once all the additions/deletions are completed, the instrument then prompts the FR/interviewer to
correct or update any relationship items (e.g., relationship
to reference person, marital status, and parent and spouse
pointers) that may be subject to change. After completion
of the appropriate corrections, the instrument will then take
the interview to any items, such as educational attainment,
that require periodic updating. The labor force interview in
MIS 2, 3, 5, 6, and 7 collects the same information as the
MIS 1 interview. MIS 4 and 8 interviews are different in
several respects. Additional information collected in these
interviews includes a battery of questions for employed
wage and salary workers on their usual weekly earnings at
their only or main job. For all individuals who are multiple
jobholders, information is collected on the industry and
occupation of their second job. For individuals who are not
in the labor force, we obtain additional information on their
previous labor force attachment.
Dependent interviewing is another enhancement contributed to the subsequent months’ interviewing by the
computerization of the labor force interview. Information
collected in the previous month’s interview is imported into
the current interview to ease response burden and improve
the quality of the labor force data. This change is most
noticeable in the collection of main job industry and occupation data. By importing the previous month’s job description into the current month’s interview, we can ascertain
whether an individual has the same job as he/she had the
preceding month. Not only does this enhance analysis of
month-to-month job mobility, it also frees the FR/interviewer
from re-entering the detailed industry and occupation descriptions. This speeds the flow through the labor force interview. Other information collected using dependent interviewing is the duration of unemployment (either job search
or layoff duration), and the not-in-labor-force subgroups of
7– 6
facilities each month. The facilities generally interview about 88-90 percent of
the cases assigned to them. The net
result is that about 15 percent of all
Include as CPS interviews are completed at a CATI
member of facility.
household
Three facilities are in use: Hagerstown,
MD; Tuscon, AZ; and JeffersonA. PERSONS STAYING IN SAMPLE UNIT AT TIME OF INTERVIEW
ville,
IN.
During the time of the initial
Person is member of family, lodger, servant, visitor, etc.
1. Ordinarily stays here all the time (sleeps here) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes
phasein of CATI data, and continuing to
2. Here temporarily - no living quarters held for person elsewhere . . . . . . . . . . . . . . . . . Yes
this day, there is a controlled selection
3. Here temporarily - living quarters held for person elsewhere . . . . . . . . . . . . . . . . . . . .
No
criteria (see Chapter 4) that allows the
Person is in Armed Forces
analysis of any effects of the CATI
1. Stationed in this locality, usually sleeps here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes
2. Temporarily here on leave - stationed elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No collection methodology. See Chapter
16 for a discussion of CATI effects on
Person is a student - Here temporarily attending school - living quarters held for
person elsewhere
the labor force data. One of the main
1. Not married or not living with immediate family. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No
reasons for using CATI is to ease the
2. Married and living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes
recruiting and hiring effort in hard to
3. Student nurse living at school . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes
enumerate areas. It is much easier to
B. ABSENT PERSON WHO USUALLY LIVES HERE IN SAMPLE UNIT
hire an individual to work in the CATI
Person is inmate of institutional special place - Absent because inmate in a
facilities than it is to hire individuals to
specified institution regardless of whether or not living quarters held for person
here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No work as FRs in most major metropolitan
Person is temporarily absent on vacation, in general hospital, etc. (including
areas. This is especially true in most
veterans’ facilities that are general hospitals) - Living quarters held here for person . Yes
large cities. Most of the cases sent to
Person is absent in connection with job
CATI are from the major metropolitan
1. Living quarters held here for person - temporarily absent while ‘‘on the road’’ in
areas. CATI is not used in most rural
connection with job (e.g., traveling salesperson, railroad conductor, bus driver) . . . Yes
2. Living quarters held here and elsewhere for person but comes here infrequently
areas because the sample sizes in these
(e.g., construction engineer) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No areas are small enough to keep a single
3. Living quarters held here at home for unmarried college student working away
FR busy the entire interview week withfrom home during summer school vacation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes
out causing any undo hardship in comPerson is in Armed Forces - was member of this household at time of induction
but currently stationed elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No pleting the assignment. A concerted effort
is made to hire some Spanish speaking
Person is a student in school - away temporarily attending school - living quarters
held for person here
interviewers in the Tuscon Telephone
1. Not married or not living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes
Center. This allows us to send Spanish
2. Married and living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No
3. Attending school overseas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No speaking cases to this facility for inter4. Student nurse living at school . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No viewing. For obvious reasons stated
above, no MIS 1 or 5 cases are sent to
C. EXCEPTIONS AND DOUBTFUL CASES
the facilities.
Person with two concurrent residences - determine length of time person has
As stated above, the facilities commaintained two concurrent residences
1. Has slept greater part of that time in another locality. . . . . . . . . . . . . . . . . . . . . . . . . . .
No plete all but 10-12 percent of the cases
2. Has slept greater part of that time in sample unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yes
sent to them. These uncompleted cases
Citizen of foreign country temporarily in the United States
are recycled back to the field for fol1. Living on premises of an Embassy, Ministry, Legation, Chancellery, or Consulate .
No
lowup and final determination. For this
2. Not living on premises of an Embassy, Ministry, etc.
reason, the CATI facilities generally cease
a. Living here and no usual place of residence elsewhere in the United States . . . . Yes
b. Visiting or traveling in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No interviewing the labor force portions of
the interview on Wednesday of interretired and disabled. Dependent interviewing is not used in
view
week.
This
allows
the field staff 3 to 4 days to check
the MIS 5 interviews nor is it used for any of the data
on
the
case
and
complete
any required interviewing or
collected solely in MIS 4 and 8.
classify the case as a noninterview. The field staff is highly
Another recent development in the computerization of
successful in completing these cases as interviews, genthe CPS is the use of centralized facilities for computer
erally interviewing about 80-85 percent of the cases. The
assisted telephone interviewing (CATI). CPS has been
cases that are sent to the CATI facilities are selected by the
experimenting with the use of CATI interviewing since 1983
supervisors in each of the regional offices. The FRs provide
and has gradually been introducing it into the CPS operainformation on a household’s probable acceptance of a
tions. The first use of CATI in production was the Tri-Cities
Test which started in April 1987. Since that time, more and
CATI interview, but ultimately it is the supervisor’s decision.
more cases have been sent to our CATI facilities for
This decision is based on the FR’s input and the need to
interviewing. Currently, about 9,200 cases are sent to the
balance workloads and meet specific goals on the number
Figure 7– 5. Summary Table for Determining Who Is To Be Included As
a Member of the Household
7– 7
Figure 7– 6. Interviews: Main Demographic Items Asked in MIS 1 and Replacement Households
Note: This list of items in not all inclusive. The list covers only the main data items and does not include related items used
to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the
actual instrument screens for all CPS items.
Part 1. Items Asked at Beginning of Interview
Item Name
Item Asks
1
2
HHRESP
RPNAME
3
4
5
NEXTNM
VERURE
HHMEM-scrn
6
7
8
SEX-scrn
MCHILD
MAWAY
9
10
11
MLODGE
MELSE
RRP-nscr
12
13
VR-NONREL
SBFAMILY
14
15
16
17
18
19
20
21
PAREN-scrn
BMON-scrn
BDAY-scrn
BYEAR-scrn
AGEVR
MARIT-scrn
SPOUS-scrn
AFEVE-scrn
AFWHE-scrn
AFNOW-scrn
22
EDUCA-scrn
23
RACE-scrn
24
ORIGI-scrn
25
SSN-scrn
26
CHANGE
What is the line number of the household respondent.
What is the name of the reference person (i.e., person who owns/rents home, whose name should appear on line number
1 of the household roster).
What is the name of the next person in the household (lines number 2 through a maximum of 16).
If the sample unit is the person’s usual place of residence.
If the person has his/her usual place of residence elsewhere; asked only when the sample unit is not the person’s usual
place of residence.
What is the person’s sex; this item is answered by the interviewer (not read to the respondent).
If the household roster (displayed on the screen) is missing any babies or small children.
If the household roster (displayed on the screen) is missing usual residents temporarily away from the unit (e.g., traveling,
at school, in a hospital).
If the household roster (displayed on the screen) is missing any lodgers, boarders, or live-in employees.
If the household roster (displayed on the screen) is missing anyone else staying in the unit.
How is the person related to the reference person; the interviewer shows the respondent a flashcard from which he/she
chooses the appropriate relationship category.
If the person is related to anyone else in the household; asked only when the person is not related to the reference person.
Who on the household roster (displayed on the screen) is the person related to; asked depending on answer to
VR-NONREL.
What is the parent’s line number.
What is the month of birth.
What is the day of birth.
What is the year of birth.
How many years old is the person (as verification).
What is the person’s marital status; asked only of persons 15+ years old.
What is the spouse’s line number; asked only of persons 15+ years old.
If the person ever served on active duty in the U.S. Armed Forces; asked only of persons 17+ years old.
When did the person serve; asked only of persons 17+ years old who have served in the U.S. Armed Forces.
If the person is now in the U.S. Armed Forces; asked only of persons 17+ years old who have served in the U.S. Armed
Forces. Interviewers will continue to ask this item each month as long as the answer is ‘‘yes.’’
What is the highest level of school completed or highest degree received; asked only of persons 15+ years old. This item
is asked for the first time in MIS 1, and then verified in MIS 5 and in specific months (i.e., February, July, and October).
What is the person’s race; the interviewer shows the respondent a flashcard from which he/she chooses the appropriate
race category.
What is the person’s origin; the interviewer shows the respondent a flashcard from which he/she chooses the appropriate
origin category.
What is the person’s social security number; asked only of persons 15+ years old. This item is asked only from December
through March, regardless of month in sample.
If there has been any change in the household roster (displayed with full demographics) since last month, particularly in
the marital status.
Part 2. Items Asked at the End of the Interview
Item Name
Item Asks
27
28
29
30
NAT1
MNAT1
FNAT1
CITZN-scr
31
32
33
CITYA-scr
CITYB-scr
INUSY-scr
34
FAMIN-scrn
What is the person’s country of birth.
What is his/her mother’s country of birth.
What is his/her father’s country of birth.
If the person is a citizen of the U.S.; asked only when neither the person nor both of his/her parents were born in the U.S.
or U.S. territory.
If the person was born a citizen of the U.S.; asked when the answer to CITZN-scr is yes.
If the person became a citizen of the U.S. through naturalization; asked when the answer to CITYA-scr is no.
When did the person come to live in the U.S.; asked of U.S. citizens born outside of the 50 states (e.g., Puerto Ricans,
U.S. Virgin Islanders, etc.) and of non-U.S. citizens.
What is the household’s total combined income during the past 12 months; the interviewer shows the respondent a
flashcard from which he/she chooses the appropriate income category.
7– 8
Figure 7– 7. Demographic Edits in the CPS Instrument
Note: The following list of edits is not all inclusive; only the major edits are described. The demographic edits in the CPS
instrument take place while the interviewer is creating or updating the roster. After the roster is in place, the interviewer may
still make changes to the roster (e.g., add/delete persons, change variables) at the Change screen. However, the instrument
does not include any more demographic edits past the Change screen, because inconsistencies—between the data
collected at that point and the data collected earlier in the interview—are difficult to resolve without risking endless loops.
Education Edits
1.
The instrument will force interviewers to probe if the education level is inconsistent with the person’s age; interviewers will probe for the correct
response if the education entry fails any of the following range checks:
• If 19 years old, the person should have an education level below the level of a master’s degree (EDUCA-scrn < 44).
• If 16-18 years old, the person should have an education level below the level of a bachelor’s degree (EDUCA-scrn 43).
• If younger than 15 years old, the person should have an education below college level (EDUCA-scrn < 40).
2.
The instrument will force the interviewer to probe before it allows him/her to lower an education level reported in a previous month in sample.
Veterans’ Edit
1.
The instrument will display only the answer categories that apply (i.e., periods of service in the Armed Forces), based on the person’s age. For
example, the instrument will not display certain answer categories for a 40 year old veteran (e.g., World War I, World War II, Korean war), but it
will display them for a 99 year old veteran.
Nativity Edits
1.
The instrument will force the interviewer to probe if the person’s year of entry into the U.S. is earlier than his/her year of birth.
Spouse Line Number Edits
1.
If the household roster does not include a spouse for the reference person, the instrument will set the reference person’s SPOUSE line number
equal to zero. It will also omit the first answer category (i.e., married spouse present) when it asks for the marital status of the reference person).
2.
The instrument will not ask SPOUSE line number for both spouses in a married couple. Once it obtains the SPOUSE line number for the first
spouse on the roster, it will fill the second spouse’s SPOUSE line number with the line number of the first spouse. Likewise, the instrument will
not ask marital status for both spouses. Once it obtains the marital status for the first spouse on the roster, it will set the second spouse’s marital
status equal to that of his/her spouse.
3.
Before assigning SPOUSE line numbers, the instrument will verify that there are opposing sex entries for each spouse. If both spouses are of the
same sex, the interviewer will be prompted to fix whichever one is incorrect.
4.
For each household member with a spouse, the instrument will ensure that his/her SPOUSE line number is not equal to his/her own line number,
nor to his/her own PARENT line number (if any). In both cases, the instrument will not allow the interviewer to make the wrong entry and will display
a message telling the interviewer to ‘‘TRY AGAIN.’’
Parent Line Number Edits
1.
The instrument will never ask for the reference person’s PARENT line number. It will set the reference person’s PARENT line number equal to
the line number of whomever on the roster was reported as the reference person’s parent (i.e., an entry of 24 at RRP-nscr), or equal to zero if
no one on the roster fits that criteria.
2.
Likewise, for each individual reported as the reference person’s child (an entry of 22 at RRP-nscr), the instrument will set his/her PARENT line
number equal to the reference person’s line number, without asking for each individual’s PARENT line number.
3.
The instrument will not allow more than two parents for the reference person.
4.
If the individual is the reference person’s brother or sister (i.e., an entry of 25 at RRP-nscr), the instrument will set his/her PARENT line number
equal to the reference person’s PARENT line number. However, the instrument will not do so without first verifying that the parent that both siblings
have in common is indeed the one whose line number appears in the reference person’s PARENT line number (since not all siblings have both
parents in common).
5.
For each household member, the instrument will ensure that his/her PARENT line number is not equal to his/her own line number. In such a case,
the instrument will not allow the interviewer to make the wrong entry and will display a message telling the interviewer to ‘‘TRY AGAIN.’’
7– 9
Figure 7– 8. Interviews: Main Items (Housing Unit and Demographic) Asked in MIS 5 Cases
Note: This list of items is not all inclusive. The list covers only the main data items and does not include related items used
to arrive at the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the
actual instrument screens for all CPS items.
Housing Unit Items
1
2
3
4
5
6
7
8
9
10
11
12
Item Name
Item Asks
HHNUM-vr
VERADD
CHNGPH
MAILAD
TENUR-scrn
TELHH-scrn
TELIN-scrn
TELPHN
BSTTM-scrn
NOSUN-scrn
THANKYOU
INOTES-1
If household is a replacement household.
What is the street address (as verification).
If current phone number needs updating.
What is the mailing address (as verification).
If unit is owned, rented, or occupied without paid rent.
If there is a telephone in the unit.
If a telephone interview is acceptable.
What is the phone number and whether it is a home or office phone.
When is the best time to contact the respondent.
If a Sunday interview is acceptable.
If there is any reason why the interviewer will not be able to interview the household next month.
If the interviewer wants to make any notes about the case that might help with the next interview; also asks for a list of
names/ages of ALL additional persons if there are more than 16 household members.
Demographic Items
Item Name
Item Asks
13
14
15
16
17
RESP1
STLLIV
NEWLIV
MCHILD
MAWAY
18
19
20
MLODGE
MELSE
EDUCA-scrn
21
CHANGE
If respondent is different from the previous interview.
If all persons listed are still living in the unit.
If anyone else is staying in the unit now.
If the household roster (displayed on the screen) is missing any babies or small children.
If the household roster (displayed on the screen) is missing usual residents temporarily away from the unit (e.g., traveling,
at school, in hospital).
If the household roster (displayed on the screen) is missing any lodgers, boarders, or live-in employees.
If the household roster (displayed on the screen) is missing anyone else staying in the unit.
What is the highest level of school completed or highest degree received; asked for the first time in MIS 1, and then verified
in MIS 5 and in specific months (i.e., February, July, and October).
If, since last month, there has been any change in the household roster (displayed with full demographics), particularly in
the marital status.
of cases sent to the facilities. The selection of cases to
send to CATI also must take into consideration statistical
aspects of the selection. With this constraint in mind,
selection of cases is controlled by a series of random group
numbers assigned at the time of sample selection that
either allow or disallow the assignment of a case to CATI.
This control procedure allows the Census Bureau to continue to analyze statistical differences that may exist between
the data collected in CATI mode versus data collected in
CAPI mode.
Figures 7– 9 and 7– 10 show the results of a typical
month’s (December 1996) CPS interviewing. Figure 7– 9
lists the outcomes of all the households in the CPS sample.
The expectations for normal monthly interviewing are a
Type A rate around 6.0 percent with an overall noninterview
rate in the 20-22 percent range. In December 1996, the
Type A rate was 5.81. For the April 1996 - March 1997
period, the CPS Type A rate was 6.36 percent. The months
of January, February, and March 1996 were not used due
to the effects of the Federal government shut downs. The
overall noninterview rate for December 1996 was 20.34
percent, compared to the 12-month average of 20.75
percent.
Figure 7– 10 shows the rates of personal and telephone
interviewing in December 1996. It is highly consistent with
the usual monthly results for personal and telephone
interviews.
7– 10
Figure 7– 9. Interviewing Results (December 1996)
Description
Total Assigned Workload . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviewed Cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Response Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviewed CAPI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
CAPI Partial Interviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Assigned CATI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviewed CATI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
CATI Partial Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
CATI Recycles to Region. . . . . . . . . . . . . . . . . . . . . . . . . . .
Noninterviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NI Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Type A Noninterviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Type A Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
No One Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Temporarily Absent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Refused . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Other Occupied . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Accessed Instr. - No Progress . . . . . . . . . . . . . . . . . . . . . .
Type B Noninterviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Type B Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Armed Forces Occupied or < age 15 . . . . . . . . . . . . . . . .
Temp. Occupied With Persons With URE . . . . . . . . . . . .
Vacant Regular (REG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vacant HHLD Furniture Storage. . . . . . . . . . . . . . . . . . . . .
Unfit, to be Demolished . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Under Construction, Not Ready . . . . . . . . . . . . . . . . . . . . .
Converted to Temp. Business or Storage. . . . . . . . . . . . .
Unoccupied Tent or Trailer Site . . . . . . . . . . . . . . . . . . . . .
Permit Granted, Construction Not Started . . . . . . . . . . . .
Other Type B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Type C Noninterviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Type C Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Demolished. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
House or Trailer moved . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Outside Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Converted to Perm. Business or Storage . . . . . . . . . . . . .
Merged . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Condemned . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Built after April 1, 1980. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unused Serial No#/Listing Sheet Line. . . . . . . . . . . . . . . .
Other Type C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60,234
47,981
94.19%
41,464
108
7,112
6,517
58
595
12,253
20.34%
2,960
5.81%
638
292
1,928
99
3
8,921
14.90%
93
1,254
6,005
374
424
259
186
274
37
15
372
0.62%
39
36
13
26
33
1
76
73
75
Figure 7– 10. Telephone Interview Rates (December 1996)
Telephone
Total . . . . . . . . . . . .
MIS 1&5 . . . . . . . .
MIS 2-4, 6-8 . . . . .
Personal
Indeterminate
Total
Number
Percent
Number
Percent
Number
Percent
47,981
11,710
36,271
34,285
2,677
31,608
71.5
22.9
87.1
13,548
8,968
4,580
28.2
76.6
12.6
148
65
83
0.3
0.6
0.2
8–1
Chapter 8.
Transmitting the Interview Results
INTRODUCTION
With the advent of the completely electronic interviewing
environment, transmission of the interview results took on
a heightened importance to the field representative (FR).
Until recently, the FR dropped the completed interviews in
the mail and waited for the U.S. Postal Service to complete
the task. Now, the data must be prepared for transmission
and sent out via computer. This chapter provides a summary of these procedures and how the data are prepared
for production processing.
The system for transmission of data is centralized at
headquarters. All data transfers must pass through headquarters even if that is not the final destination of the
information. The system was designed this way for ease of
management and to ensure uniformity of procedures within
a given survey and between different surveys. The transmission system was designed to satisfy the following
requirements:
• Provide minimal user intervention.
• Upload and/or download in one transmission.
• Transmit all surveys in one transmission.
• Transmit software upgrades with data.
• Maintain integrity of software and assignment.
• Prevent unauthorized access.
• Handle mail messages.
Another important aspect about the system is the headquarters central database system cannot initiate transmissions. Either the FR or the regional offices (ROs) must
initiate any transmissions. Computers in the field are not
connected to the headquarters computers at all times.
Instead, the field computers contact the headquarters
computers and all data exchanges take place at the time of
such call-ins. The central database system contains a
group of servers that store messages and case information
required by the FRs or the ROs. When an interviewer calls
in, the transfer of data from the FR’s computer to headquarters computers is completed first and then any outgoing data are transferred to the FR’s computer.
A major concern with the use of a electronic method of
transmitting interview data is the need for complete security of these data. Both the Census Bureau and the Bureau
of Labor Statistics (BLS) are required to honor the pledge
of confidentiality given to all Current Population Survey
(CPS) respondents. The system was designed to safeguard this pledge. All transmissions between the headquarters central database and the FR’s computers are compacted and encrypted for this reason. All transmissions
between the headquarters, the ROs, the Centralized Telephone Facilities, and Jeffersonville are over secure telecommunications lines that are leased by the Census
Bureau and are accessible only by Census Bureau employees through their computers.
TRANSMISSION OF INTERVIEW DATA
Telecommunications exchanges between a FR and the
central database system usually take place once per day
during the interview period. Additional transmissions may
be made at any time as needed. Each transmission is a
batch process in which all relevant files are automatically
transmitted and received.
Each FR is expected to make a telecommunications
transmission at the end of every work day during the
interview period. This is usually accomplished by a preset
transmission; that is, each evening the FR sets the FR’s
computer up to transmit the completed work and then
hooks the computer up to a modem within the FR’s
residence. During the night, at a preset time, the computer
automatically dials into the headquarters central database
and transmits the completed cases. At the same time, the
central database returns any messages and other data to
complete the FR’s assignment. It is also possible for a FR
to make an immediate transmission at any time of the day.
The results of such a transmission are identical to a preset
transmission, both in the types and directions of various
data transfers, but allow the FR instant access to the
central database as necessary. This type of procedure is
used primarily around the time of closeout when a FR
might have one or two straggler cases that need to be
received by headquarters before field staff can close out
the month’s workload and production processing can begin.
The RO staff may also perform a daily data transmission
sending in cases that required supervisory review or were
completed at the RO.
Centralized Telephone Facility Transmission
Most of the cases sent to a Centralized Telephone
Facility are successfully completed as computer assisted
telephone interviews (CATI). Those that cannot be completed from the telephone center are transferred to a FR
8– 2
prior to the end of the interview period. These cases are
called ‘‘CATI recycles.’’ Each telephone facility makes daily
transmissions of completed cases and recycles to the
headquarters database. All the completed cases are batched
for further processing. Each recycled case is transmitted
directly to the computer of the FR who is assigned the
case. Case notes that include the reason for recycle are
also transmitted to the FR to assist in follow-up. CATI
recycles are placed on the server when a daily transmission is performed for the region. Daily transmissions are
performed automatically for each region every hour during
the CPS interview week. This provides cases to the FRs
that are reassigned or recycled.
The RO staff also monitor the progress of the CATI
recycled cases Recycle Report. All cases that are sent to a
CATI facility are also assigned to a FR by the RO staff. The
RO staff keep a close eye on recycled cases to ensure that
they are completed on time and to monitor the reasons for
recycling so that future recycling can be minimized. The
reason for recycling is also closely monitored to ensure that
recycled cases are properly handled by the CATI facility
and correctly identified as CATI eligible by the FR.
Transmission of Interviewed Data From the Centralized
Database
Each day during the production cycle (see Figure 8-1 for
an overview of the daily processing cycle) the field staff
send to the production processing system at headquarters
four files containing the results of the previous day’s
interviewing. A separate file is received from each of the
CATI facilities, and all data received from the FRs are
batched together and sent as a single file. At this time,
cases requiring industry and occupation coding (I&O) are
identified, and a file of such cases is created. This file is
then used by Jeffersonville coders to assign the appropriate I&O codes. This cycle repeats itself until all data are
received by headquarters, usually Tuesday or Wednesday
of the week after interviewing begins. By the middle of the
interview week, the CATI facilities close down, usually
Wednesday, and only one file is received daily by headquarters production processing system. This continues
until field closeout day when multiple files may be sent to
expedite the preprocessing.
Data Transmissions for I&O Coding
The I&O data are not actually transmitted to Jeffersonville. Rather, the coding staff directly access the data on
headquarters computers through the use of remote monitors in Jeffersonville. When a batch of data has been
completely coded, that file is returned to headquarters, and
the appropriate data are loaded into the headquarters
database. See Chapter 9 for a complete overview of the
I&O coding and processing system.
Once these transmission operations have been completed, final production processing begins. Chapter 9 provides a description of the processing operation.
Figure 8– 1. Overview of CPS Monthly Operations
Week 1
(week containing
the 5th)
Thu
Fri
Sat
Field reps pick up
computerized
questionnaire via
modem
Week 2
(week containing the 12th)
Sun
Mon
Tue
Wed
Field reps and CATI
interviewers complete
practice interviews and
home studies
Thu
Fri
Week 3
(week containing the 19th)
Sat
Field reps pick up
assignments via
modem
Sun
Mon
Tue
CATI interviewing
CATI facilities
transmit
completed work to
headquarters nightly
Wed
CATI
interviewing
ends*
Thu
Fri
Week 4
(week containing the 26th)
Sat
Sun
All
assignments
completed
except for
telephone
holds
Mon
Tue
All
interviews
completed
ROs
complete
final
field
closeout
CAPI interviewing
● FRs transmit completed work to
headquarters nightly
● ROs monitor interviewing assignments
Processing begins
● Files received overnight are checked in by ROs and
headquarters daily
● Headquarters performs initial processing and sends eligible
cases to Jeffersonville for industry and occupation coding
Jeffersonville sends back completed I&O cases
Wed
Thu
Fri
Week 5
Sat
Sun
thru
Thu
Fri
Initial
process
closeout
J’ville
closeout
Headquarters performs
final computer Census
processing
delivers data
● Edits
to BLS
● Recodes
● Weighting
BLS
analyzes
data
Results
released
to
public
at
8:30
am
by
BLS
* CATI interviewing is extended in March and certain other months.
8– 3
9–1
Chapter 9.
Data Preparation
INTRODUCTION
INDUSTRY AND OCCUPATION (I&O) CODING
For the Current Population Survey (CPS), the goal of
post data collection processing is to transform a raw data
file, as collected by interviewers, into a microdata file that
can be used to produce estimates. There are several
processes needed in order for this to be accomplished. The
raw data files must be read and processed. Textual industry
and occupation responses are coded. Even though some
editing takes place in the instrument at the time of interview
(see Chapter 7), further editing is required once all the data
are received. Editing and imputations are performed to
improve the consistency and completeness of the microdata. New data items are created based upon responses to
multiple questions. All of this is in preparation for weighting
and estimation (see Chapter 10).
The industry and occupation coding operation for a
typical month requires ten coders for a period of just over 1
week to code data from 27,000 individuals.1 At other times,
these same coders are available for similar activities on
other surveys, where their skills can be maintained. The
volume of codes has decreased significantly with the
introduction of dependent interviewing for I&O codes (see
Chapter 6). Only the new monthly CPS cases, as well as
those persons whose industry or occupation has changed
since the previous month of interviewing, are sent to
Jeffersonville to be coded. For those persons whose
industry and occupation have not changed, the three-digit
codes are brought forward from the previous month of
interviewing and require no further coding.
A computer assisted industry and occupation coding
system is used by the Jeffersonville I&O coders. Files of all
eligible I&O cases are sent to this system each day. Each
coder works at a computer terminal where the computer
screen displays the industry and occupation descriptions
that were captured by the field representatives at the time
of the interview. The coder then enters three-digit numeric
industry and occupation codes used in the 1990 census
that represent the industry and occupation descriptions.
A substantial effort is directed at supervision and control
of the quality of this operation. The supervisor is able to
turn the dependent verification setting ‘‘on’’ or ‘‘off’’ at any
time during the coding operation. The ‘‘on’’ mode means
that a particular coder’s work is verified by a second coder.
In addition, a 10 percent sample of each month’s cases is
selected to go through a quality assurance system to
evaluate the work of each coder. The selected cases are
verified by another coder after the current monthly processing has been completed.
After this operation is complete, the batch of records is
electronically returned to headquarters to be used in the
monthly production processing.
DAILY PROCESSING
For a typical month, computer assisted telephone interviewing (CATI) starts on Sunday of the week containing the
19th of the month and continues through Wednesday of the
same week. The answer files representing these interviews
are received on a daily basis from Monday through Thursday of this interview week. Separate files are received for
each of the three CATI facilities: Hagerstown, Tucson, and
Jeffersonville. Computer assisted personal interviewing
(CAPI) also begins on the same Sunday and continues
through Monday of the following week. The CAPI answer
files are received for each day of interviewing, until all the
interviewers and regional offices have transmitted the
workload for the month. This is generally completed by
Wednesday of the following week. These answer files are
read, and various computer checks are performed to
ensure the data can be accepted into the CPS processing
system. These checks include, but are not limited to,
ensuring the successful transmission and receipt of the
files, item range checks, and rejection of invalid cases.
Files containing records needing three-digit industry and
occupation (I&O) codes are electronically sent to Jeffersonville for assignment of these codes. Once Jeffersonville has
completed the I&O coding, the files are electronically
transferred back to headquarters, where the codes are
placed on the CPS production file. When all of the expected
data for the month are accounted for and all of Jeffersonville’s I&O coding files have been returned and placed on
the appropriate records on the data file, editing and imputation are performed.
EDITS AND IMPUTATIONS
The CPS suffers from two sources of nonresponse. The
largest results from noninterview households. We compensate for this data loss in the weighting where essentially the
1
Because of the CPS sample increase in July 2001 from the State
Children’s Health Insurance Program, the number of cases for I&O coding
has increased to about 30,000.
9–2
weights of noninterviewed households are distributed among
interviewed households (see Chapter 10). The second
source of data loss is from item nonresponse. Item nonresponse occurs when a respondent either does not know
the answer to a question or refuses to provide the answer.
Item nonresponse in the CPS is modest (see Chapter 16).
We compensate for item nonresponse in the CPS by
using 1 of 3 imputation methods. Before the edits are
applied, the daily data files are merged and the combined
file is sorted by state and PSU within state. This sort
ensures that allocated values are from geographically
related records; that is, missing values for records in
Maryland will not receive values from records in California.
This is an important distinction since many labor force and
industry and occupation characteristics are geographically
clustered.
The edits effectively blank all entries in inappropriate
questions and ensure that all appropriate questions have
valid entries. For the most part, illogical entries or out-ofrange entries have been eliminated since the use of
electronic instruments; however, the edits still address
these possibilities due to data transmission problems and
occasional instrument malfunctions. The main purpose of
the edits, however, is to assign values to questions where
the response was ‘‘Don’t know’’ or ‘‘Refused.’’ This is
accomplished by using 1 of 3 imputation techniques described
below.
Before discussing these imputation techniques, it is
important to note that the edits are run in a deliberate and
logical sequence. That is, demographic variables are edited
first because several of those variables are used to allocate
missing values in the other modules. The labor force
module is edited next since labor force status and related
items are used to impute missing values for industry and
occupation codes and so forth.
The three imputation methods used by the CPS edits
are described below.
1.
Relational imputation infers the missing value from
other characteristics on the person’s record or within
the household. For instance, if race is missing, it is
assigned based on the race of another household
member or failing that, taken from the previous record
on the file. Similarly, if relationship is missing, it is
assigned by looking at age and sex of the person in
conjunction with the known relationship of other household members. Missing occupation codes are sometimes assigned by viewing the industry codes and vice
versa. This technique is used exclusively in the demographic and industry and occupation edits. If missing
values cannot be assigned using this technique, they
are assigned using 1 of the 2 following methods.
2. Longitudinal edits are used primarily in the labor force
edits. If a question is blank and the record is in the
overlap sample, the edit looks at last month’s data to
determine whether there was a nonallocated entry for
that item. If so, last month’s entry is assigned; otherwise, the item is assigned a value using the appropriate hot deck, as described next.
3. This imputation method is commonly referred to as
‘‘hot deck’’ allocation. This method assigns a missing
value from a record with similar characteristics. Hot
decks are always defined by age, race, and sex . Other
characteristics used in hot decks vary depending on
the nature of the question being referenced. For
instance, most labor force questions use only age,
race, sex, and occasionally another labor force item
such as full- or part-time status. This means the
number of cells in labor force item hot decks are
relatively small, perhaps less than 100. On the other
hand, the weekly earnings hot deck is defined by age
race, sex, usual hours, occupation, and educational
attainment. This hot deck has several thousand cells.
All CPS items that require imputation for missing values
have an associated hot deck . The initial values for the hot
decks are the ending values from the preceding month. As
a record passes through the edits, it will either donate a
value to each hot deck in its path or receive a value from
the hot deck. For instance, in a hypothetical case, the hot
deck for question X is defined by the characteristics
Black/non-Black, male/female, and age 16-25/25+. Further
assume a record has the value of White, male, and age 64.
When this record reaches question X, the edits determine
whether it has a valid entry. If so, that record’s value for
question X replaces the value in the hot deck reserved for
non-Black, male, and age 25+. Comparably, if the record
was missing a value for item X, it would be assigned the
value in the hot deck designated for non-Black, male, and
age 25+.
As stated above the various edits are logically sequenced,
in accordance with the needs of subsequent edits. The
edits and codes, in order of sequence, are:
1. Household edits and codes. This processing step
performs edits and creates recodes for items pertaining to the household. It classifies households as
interviews or noninterviews and edits items appropriately. Hot deck allocations defined by geography are
used in this edit.
2. Demographic edits and codes. This processing step
ensures consistency between all demographic variables for all individuals within a household. It ensures
all interview households have one and only one reference person and that entries stating marital status,
spouse, and parents are all consistent. It also creates
families based upon these characteristics. It uses
longitudinal editing, hot deck allocation defined by
related demographic characteristics, and relational
imputation.
Demographic related recodes are created for both
individual and family characteristics.
9–3
3. Labor force edits and codes. This processing step
first establishes an edited Major Labor Force Recode
(MLR), which classifies adults as either employed,
unemployed, or not in the labor force.
Based upon MLR, the labor force items related to
each series of classification are edited. This edit uses
longitudinal editing and hot deck allocation matrices.
The hot decks are defined by age, race, and sex and,
on occasion, by a related labor force characteristic.
4. Industry and occupation (I&O) edits and codes.
This processing step assigns three-digit industry and
occupation codes to those I&O eligible persons for
which the I&O coders were unable to assign a code. It
also ensures consistency, wherever feasible, between
industry, occupation, and class of worker. I&O related
recodes are also created. This edit uses relational
allocation and hot deck allocation. The hot decks are
defined by employment status, age, sex, race, and
educational attainment.
5. Earnings edits and codes. This processing step
performs edits on the earnings series of items for
earnings eligible individuals. A usual weekly earnings
recode is created to allow earnings amounts to be in a
comparable form for all eligible individuals. There is no
longitudinal editing because this series of questions is
asked only of MIS 4 and 8 households. Hot deck
allocation is used here. The hot deck for weekly
earnings is defined by age, race, sex, major occupation recode, educational attainment, and usual hours
worked. Additional earnings recodes are created.
6. School enrollment edits and codes. School enrollment items are edited for individuals 16-24 years old.
Hot deck allocation based on age, race, and sex is
used.
10–1
Chapter 10.
Estimation Procedures for Labor Force Data
INTRODUCTION
The Current Population Survey (CPS) is a multistage
probability sample of housing units in the United States. It
produces monthly labor force and related estimates for the
total U.S. civilian noninstitutional population and for various
age, sex, race, and ethnic groups. In addition, estimates for
a number of other population subdomains of the nation
(e.g., families, veterans, persons with earnings, households) are produced on either a monthly or quarterly basis.
Each month a sample of eight panels (called rotation
groups) is interviewed, with demographic data collected for
all occupants of the sample housing units. Labor force data
are collected for persons 15 years and older. Each rotation
group is itself a representative sample of the U.S. population. The labor force estimates are derived through a
number of steps in the estimation procedure.
The weighting procedures of the supplements are discussed in Chapter 11. The supplements tend to have
higher nonresponse rates. In addition, many of the supplements apply to specific demographic subpopulations and
thus differ in coverage from the basic CPS universe.
In order to produce national and state estimates from
survey data, a weight for each person in the sample is
developed through the following steps:
• Preparation of simple unbiased estimates from baseweights and special weights derived from CPS sampling
probabilities.
• Adjustment for nonresponse.
• First-stage ratio adjustment to reduce variances due to
the sampling of PSUs.
• Second-stage ratio adjustment to reduce variances by
controlling CPS estimates of population to independent
estimates of the current population.
• Composite estimation which uses survey data from previous months to reduce the variances.
• Seasonal adjustment for key labor force statistics.
In addition to estimates of basic labor force characteristics, several other types of estimates are also produced
either on a monthly or a quarterly basis. These include:
• Household-level estimates and estimates of married
couples living in the same household using household
and family weights.
• Estimates of earnings, union affiliation, and industry and
occupation of second jobs collected from respondents in
the quarter sample using outgoing rotation group weights.
• Estimates of labor force status by age for veterans and
nonveterans using veterans’ weights.
• Estimates of monthly gross flows using longitudinal weights.
The additional estimation procedures provide highly
accurate estimates for particular subdomains of the civilian
noninstitutional population. The processes described in this
chapter have remained essentially unchanged since January 1978. Seasonal adjustment for selected labor force
categories has been a part of the estimation procedure
since June 1975. Some of these processes have been
slightly modified after each decennial census, when the
CPS sample is restratified and a new set of sample PSUs
is identified. Modifications have been made in the noninterview adjustment and the source and scope of the
independent estimates of current population used in the
second-stage ratio adjustment. In January 1998, a new
compositing procedure was introduced. (See Appendix I.)
UNBIASED ESTIMATION PROCEDURE
A probability sample is defined as a sample that has a
known nonzero probability of selection for each sample
unit. With probability samples, unbiased estimators can be
obtained. These are estimates that on average, over
repeated samples, yield the population values.
An unbiased estimator of the population total for any
characteristic investigated in the survey may be obtained
by multiplying the value of that characteristic for each
sample unit (person or household) by the reciprocal of the
probability with which that unit was selected and summing
the products over all units in the sample (Hansen, 1953).
By starting with unbiased estimates from a probability
sample, various kinds of estimation and adjustment procedures (such as for noninterview) can be applied with
reasonable assurance that the overall accuracy of the
estimates will be improved.
In the CPS sample for any given month, not all units
respond, and this nonresponse is a potential source of
bias. This nonresponse averages about 6 to 7 percent.
Other factors, such as occasional errors occurring in the
sample selection procedure and households or persons
missed by interviewers, can also introduce bias. These
10–2
missing households or persons can be considered as
having zero probability of selection. These two exceptions
not withstanding, the probability of selecting each unit in
the CPS is known, and every attempt is made to keep
departures from true probability sampling to a minimum.
If all units in a sample have the same probability of
selection, the sample is called self-weighting, and unbiased estimators can be computed by multiplying sample
totals by the reciprocal of this probability. Most of the state
samples in the CPS come close to being self-weighting.
Basic Weighting
The sample designated for the 792-area design was
selected with probabilities equal to the inverse of the
required state sampling intervals shown in Table 3–1.
These sampling intervals are called the basic weights (or
baseweights). Almost all sample persons within the same
state have the same probability of selection. Exceptions
include sample persons in New York and California, where
households in New York City and Los Angeles-Long Beach
Metropolitan area are selected with higher probability than
those in the remainder of these two states. As the first step
in the estimation procedure, raw counts from the sample
housing units are multiplied by the baseweights. Every
person in the same housing unit receives the same baseweight.
Effect of Sample Reductions on Basic Weights
As time goes on, the number of households and the
population as a whole increases, and continued use of the
original sampling interval leads to a larger sample size
(with an increase in costs). The sampling interval is regularly adjusted (the probability of selection is reduced) in
order to maintain a fairly constant sample size and cost
(see Appendix C).
Special Weighting Adjustments
As discussed in Chapter 3, some ultimate sampling units
(USUs) are subsampled in the field, because their observed
size is much larger than the expected four housing units.
During the estimation procedure, housing units in these
USUs must receive special weighting factors to account for
the change in their probability of selection. For example, an
area sample USU expected to have 4 housing units (HUs)
but found at the time of interview to contain 36 HUs, could
be subsampled at the rate of 1 in 3 to reduce the interviewer’s workload. Each of the 12 designated housing units in
this case would be given a special weighting factor of 3. In
order to limit the effect of this adjustment on the variance of
sample estimates, these special weighting factors are
limited to a maximum value of 4. At this stage of CPS
estimation process, the special weighting factors are multiplied by the baseweights. The resulting weights are then
used to produce ‘‘unbiased’’ estimates. Although this estimate is commonly called ‘‘unbiased,’’ it does still include
some negligible bias because the size of the special
weighting factor is limited to 4. The purpose of this limitation is to achieve a compromise between an increase in the
bias and the variance.
ADJUSTMENT FOR NONRESPONSE
Nonresponse arises when households or other units of
observation that have been selected for inclusion in a
survey fail to yield all or some of the data that were to be
collected. This failure to obtain complete results from all the
units selected can arise from several different sources,
depending upon the survey situation. There are two major
types of nonresponse: item nonresponse and complete (or
unit) nonresponse. Unit nonresponse refers to the failure to
collect any survey data from an occupied sample unit. For
example, data may not be obtained from an eligible household in the survey because of respondent’s absence,
impassable roads, refusal to participate in the interview, or
unavailability of the respondents for other reasons. This
type of nonresponse in the CPS is called a Type A
noninterview. Historically, between 4 and 5 percent of the
eligible units in a given month were Type A noninterviews.
Recently, the Type A rate has risen to between 6 and 7
percent (see Chapter 16). Item nonresponse occurs when
a cooperating unit fails or refuses to provide some specific
items of information. Procedures for dealing with this type
of nonresponse are discussed in Chapter 9.
In the CPS estimation process, the weights for all
interviewed households are adjusted to account for occupied sample households for which no information was
obtained because of unit nonresponse (Type A noninterviews). This noninterview adjustment is made separately
for similar sample areas that are usually, but not necessarily, contained within the same state. Increasing the weights
of interviewed sample units to account for eligible sample
units which are not interviewed assumes that the interviewed units are similar to the noninterviewed units with
regard to their demographic and socioeconomic characteristics. This may or may not be true. Nonresponse bias
results when the nonresponding units differ in relevant
respects from those which respond to the survey or to the
particular items.
Noninterview Clusters and Noninterview
Adjustment Cells
To reduce the size of the bias, the noninterview adjustment is performed within sample PSUs that are similar in
MSA (metropolitan statistical area ) status and MSA size.
These PSUs are grouped together to form noninterview
clusters. In general, PSUs belonging to MSAs of the same
10–3
(or similar) size in the same state belong to the same
noninterview cluster. PSUs classified as MSA are assigned
to MSA clusters. Likewise, non-MSA PSUs are assigned to
non-MSA clusters. Within each cluster, there is a further
breakdown into two noninterview adjustment cells (also
called residence cells). Each MSA cluster is split into
‘‘central city’’ and ‘‘not central city’’ cells. Each non-MSA
cluster is divided into ‘‘urban’’ and ‘‘rural’’ cells, making a
total of 254 adjustment cells (or 127 noninterview clusters).
Computing Noninterview Adjustment Factors
Weighted counts of interviewed and noninterviewed
households are tabulated separately for each noninterview
adjustment cell. The basic weight multiplied by any special
weighting factor is used as the weight for this purpose. The
noninterview factor Fij is computed as:
Fij ⫽
Zij⫹ Nij
Zij
where
Z ij =
is the weighted count of interviewed households in
cell j of cluster i, and
N ij ⫽ is the weighted count of Type A noninterviewed
households in cell j of cluster i.
RATIO ESTIMATION
Distributions of demographic characteristics derived from
the CPS sample in any month will be somewhat different
from the true distributions even for such basic characteristics as age, race, sex, and Hispanic1 ethnicity. These
particular population characteristics are closely correlated
with labor force status and other characteristics estimated
from the sample. Therefore, the variance of sample estimates based on these characteristics can be reduced
when, by the use of appropriate weighting adjustments, the
sample population distribution is brought as closely into
agreement as possible with the known distribution of the
entire population with respect to these characteristics. This
is accomplished by means of ratio adjustments. There are
two ratio adjustments in the CPS estimation process: the
first-stage ratio adjustment and the second-stage ratio
adjustment.
In the first-stage ratio adjustment, weights are adjusted
so that the distribution of Black and non-Black census
population from the sample PSUs in a state corresponds to
the Black/non-Black population distribution from the census for all PSUs in the state. In the second-stage ratio
adjustment, weights are adjusted so that aggregated CPS
sample estimates match independent estimates of population in various age/sex/race and age/sex/ethnicity cells at
the national level. Adjustments are also made so that the
estimated state populations from CPS match independent
state population estimates.
These factors are applied to data for each interviewed
person except in cells where either of the following situations occur:
FIRST-STAGE RATIO ADJUSTMENT
• The computed factor is greater than or equal to 2.0.
Purpose of the First-Stage Ratio Adjustment
• There are fewer than 50 unweighted interviewed households in the cell.
If any of these situations occur, the weighted counts are
combined for the residence cells within the noninterview
cluster. A common adjustment factor is computed and
applied to weights for interviewed persons within the
cluster. Generally, fewer than 10 noninterview clusters
require this type of collapsing in a given month.
Weights After the Noninterview Adjustment
At the completion of the noninterview adjustment procedure, the weight for each interviewed person is:
(baseweight) x (special weighting factor) x
(noninterview adjustment factor)
At this point, records for all individuals in the same household have the same weight, since the adjustments discussed so far depend only on household characteristics.
The purpose of the first-stage ratio adjustment is to
reduce the contribution to the variance of sample statelevel estimates arising from the sampling of PSUs. That is,
the variance that would still be associated with the statelevel estimates even if we included in the survey all
households in every sample PSU. This is called the
between-PSU variance. For some states, the betweenPSU variance makes up a relatively large proportion of the
total variance, while the relative contribution of the betweenPSU variance at the national level is generally quite small.
As can be seen in Table 14–3 the first-stage ratio adjustment causes a rise in the national relative variance factor,
but the second-stage ratio adjustment decreases the relative variance factor below that of the noninterview adjustment. Further research into the effect of the combined firstand second-stage ratio adjustment is needed to determine
whether the first-stage ratio adjustment is in fact meeting
its purpose.
1
Hispanics may be of any race.
10–4
There are several factors to be considered in determining what information to use in applying the first-stage
adjustment. The information must be available for each
PSU, correlated with as many of the statistics of importance published from the CPS as possible, and reasonably
stable over time so that the gain from the ratio adjustment
procedure does not deteriorate. The basic labor force
categories (unemployed, nonagricultural employed, etc.)
could be considered. However, this information could badly
fail the stability criterion. The distribution of population by
race (Black/non-Black) satisfies all three criteria.
By using the Black/non-Black categories, the first-stage
ratio adjustment compensates for the fact that the racial
composition of an NSR (nonself-representing) sample PSU
could differ substantially from the racial composition of the
stratum it is representing. This adjustment is not necessary
for SR (self-representing) PSUs since they represent only
themselves.
␲ sk
=
1990 probability of selection for sample PSU k
in state s.
n
⫽
total number of NSR PSUs (sample and nonsample) in state s.
m
=
number of sample NSR PSUs in state s.
The estimate in the denominator of each of the ratios is
obtained by multiplying the 1990 census population in the
appropriate race cell for each NSR sample PSU by the
inverse of the probability of selection for that PSU and
summing over all NSR sample PSUs in the state.
The Black and non-Black cells are collapsed within a
state when a cell meets one of the following criteria:
• The factor (FSsj) is greater than 1.3.
• The factor is less than 1/1.3=.769230.
Computing First-Stage Ratio Adjustment Factors
• There are fewer than 4 NSR sample PSUs in the state.
The first-stage adjustment factors are based on 1990
census data and are applied only to sample data for the
NSR PSUs. Factors are computed for the two race categories (Black, non-Black) for each state containing NSR
PSUs. The following formula is used to compute the
first-stage adjustment factors for each state:
• There are fewer than ten expected interviews in a race
cell in the state.
Race cells are collapsed within 23 states, resulting in
first-stage factors of 1.0 for these states. Eight states are
self-representing and have first-stage factors of 1.0 by
definition. (See Table 10–1).
n
FSsj ⫽
兺 Csij
i⫽1
m
兺
冋 册
k⫽1
1
␲sk
共Cskj兲
Weights After First-Stage Ratio Adjustment
At the completion of the first-stage ratio adjustment, the
weight for each responding person is the product of:
where
FSsj ⫽
the first-stage factor for state s and race cell j
(j=Black, non-Black).
Csij
⫽
the 1990 16+ census population for NSR PSU i
(sample or nonsample) in state s, race cell j.
Cskj
⫽
the 1990 16+ census population for NSR sample
PSU k in state s, race cell j.
(baseweight) x (special weighting adjustment factor) x
(noninterview adjustment factor) x
(first-stage ratio adjustment factor).
The weight after the first-stage adjustment is called the
first-stage weight. Records for all individuals in the same
household should have the same first-stage weight, except
for mixed-race households having some members who are
Black and others who are non-Black.
10–5
Table 10–1: State First-Stage Factors for CPS 1990
Design (September - December 1995)
State
Alabama . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arkansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
California . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Connecticut . . . . . . . . . . . . . . . . . . . . . . . . . . .
Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
District of Columbia . . . . . . . . . . . . . . . . . . . .
Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Indiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kentucky . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Louisiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maryland . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Missouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Montana . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nebraska . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nevada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Hampshire . . . . . . . . . . . . . . . . . . . . . . .
New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Mexico . . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . . . .
North Dakota . . . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oklahoma . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oregon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . .
Rhode Island . . . . . . . . . . . . . . . . . . . . . . . . .
South Carolina . . . . . . . . . . . . . . . . . . . . . . . .
South Dakota . . . . . . . . . . . . . . . . . . . . . . . . .
Tennessee . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Texas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Utah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Washington . . . . . . . . . . . . . . . . . . . . . . . . . . .
West Virginia . . . . . . . . . . . . . . . . . . . . . . . . .
Wisconsin . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wyoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black
Non-Black
0.92986976
1.00000000
1.00000000
1.03854268
0.92824011
1.00000000
1.00000000
1.00000000
1.00000000
1.07779736
1.06981965
1.00000000
1.00000000
1.01947743
1.16715920
1.00000000
1.00000000
1.09352656
1.04956759
1.00000000
1.00000000
1.00000000
0.92441097
1.00000000
0.98243024
1.00000000
1.00000000
1.00000000
1.00000000
1.00000000
1.00000000
1.00000000
0.83647167
1.07378643
1.00000000
0.97362223
1.01775196
1.00000000
1.17560284
1.00000000
0.93971915
1.00000000
1.08638935
1.23277658
1.00000000
1.00000000
1.00000000
1.00000000
1.22587622
1.00000000
1.00000000
1.02360321
1.00000000**
1.00000000**
0.99625041
1.01550280
1.00000000**
1.00000000*
1.00000000*
1.00000000*
1.00025192
0.98237807
1.00000000**
1.00000000**
1.00254363
0.99747055
1.00000000**
1.00000000**
0.99897341
0.98344772
1.00000000**
1.00000000**
1.00000000*
0.99798724
1.00000000**
1.00997154
1.00000000**
1.00000000**
1.00000000**
1.00000000**
1.00000000*
1.00000000*
1.00000000**
1.00163779
0.98057928
1.00000000**
1.00000085
1.00400963
1.00000000**
0.99367587
1.00000000*
1.05454366
1.00000000**
0.98680199
0.97475648
1.00000000**
1.00000000*
1.00000000**
1.00000000**
0.99959222
1.00000000**
1.00000000**
* These states contain only SR PSUs in the 1990 sample design and
have an implied first-stage factor of 1.000000.
** Race cells were collapsed.
SECOND-STAGE RATIO ADJUSTMENT
The second-stage ratio adjustment decreases the error
in the great majority of sample estimates. Chapter 14
illustrates the amount of reduction in variance for key labor
force estimates. The procedure is also believed to reduce
the bias due to coverage errors (see Chapter 15). The
procedure adjusts the weights for sample records within
each rotation group to control the sample estimates for a
number of geographic and demographic subgroups of the
population to ensure that these sample-based estimates of
population match independent population controls in each
of these categories. These independent population controls are updated each month. Three sets of controls are
used:
• The civilian noninstitutional population 16 years of age
and older for the 50 states and the District of Columbia.
• Total national civilian noninstitutional population for 14
Hispanic and 5 non-Hispanic age-sex categories (see
Table 10–2).
• Total national civilian noninstitutional population for 66
white, 42 Black, and 10 ‘‘Other’’ age-sex categories (see
Table 10–3).
The adjustment is done separately for each of the eight
rotation groups which comprise a monthly sample. Adjusting the weights to match one set of controls can cause
differences in other controls, so an iterative process is used
to simultaneously control all variables. Successive iterations begin with the weights as adjusted by all previous
iterations. A total of six iterations is performed, which
results in virtual consistency between the sample estimatesandpopulationcontrols.Thethree-way(state,Hispanic/sex/age,
race/sex/age) raking ratio estimator is also known as
iterative proportional fitting.
In addition to reducing the error in many CPS estimates
and converging to the population controls within six iterations for most items, the raking ratio estimator has another
desirable property. When it converges, this estimator minimizes the statistic
兺i W2iIn共W2i ⲐW1i兲,
where
W2i ⫽ the final weight for the ith sample record, and
W1i ⫽ the weight for the ith record after the first-stage
adjustment.
Thus, the raking ratio estimator adjusts the weights of the
records so that the sample estimates converge to the
population controls while, in some sense, minimally affecting the first-stage weights. The reference (Ireland and
Kullback, 1968) provides more details on the properties of
raking ratio estimation.
10–6
Table 10–2. Initial Cells for Hispanic/Non-Hispanic by Age and Sex
Hispanic1
Ages
Non-Hispanic
Ages
Male
Female
0-5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6-13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20-29 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30-49 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50+. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*0-5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*6-13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*16+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
*No distinction is made between sexes.
* No distinction is made between sexes.
1
Hispanics may be of any race.
Table 10–3. Initial Cells for Black/White/Other by Age, Race, and Sex
Age
Black
male
Black
female
0-1 . . . . . . . . . . . . . . . . . . . . . . .
2-3 . . . . . . . . . . . . . . . . . . . . . . .
4-5 . . . . . . . . . . . . . . . . . . . . . . .
6-7 . . . . . . . . . . . . . . . . . . . . . . .
8-9 . . . . . . . . . . . . . . . . . . . . . . .
10-11 . . . . . . . . . . . . . . . . . . . . .
12-13 . . . . . . . . . . . . . . . . . . . . .
14 . . . . . . . . . . . . . . . . . . . . . . . .
15 . . . . . . . . . . . . . . . . . . . . . . . .
16-17 . . . . . . . . . . . . . . . . . . . . .
18-19 . . . . . . . . . . . . . . . . . . . . .
20-24 . . . . . . . . . . . . . . . . . . . . .
25-29 . . . . . . . . . . . . . . . . . . . . .
30-34 . . . . . . . . . . . . . . . . . . . . .
35-39 . . . . . . . . . . . . . . . . . . . . .
40-44 . . . . . . . . . . . . . . . . . . . . .
45-49 . . . . . . . . . . . . . . . . . . . . .
50-54 . . . . . . . . . . . . . . . . . . . . .
55-59 . . . . . . . . . . . . . . . . . . . . .
60-64 . . . . . . . . . . . . . . . . . . . . .
65+ . . . . . . . . . . . . . . . . . . . . . . .
White
male
Age
0......................
1......................
2......................
3......................
4......................
5......................
6......................
7......................
8......................
9......................
10-11 . . . . . . . . . . . . . . . . . .
12-13 . . . . . . . . . . . . . . . . . .
14 . . . . . . . . . . . . . . . . . . . . .
15 . . . . . . . . . . . . . . . . . . . . .
16 . . . . . . . . . . . . . . . . . . . . .
17 . . . . . . . . . . . . . . . . . . . . .
18 . . . . . . . . . . . . . . . . . . . . .
19 . . . . . . . . . . . . . . . . . . . . .
20-24 . . . . . . . . . . . . . . . . . .
25-26 . . . . . . . . . . . . . . . . . .
27-29 . . . . . . . . . . . . . . . . . .
30-34 . . . . . . . . . . . . . . . . . .
35-39 . . . . . . . . . . . . . . . . . .
40-44 . . . . . . . . . . . . . . . . . .
45-49 . . . . . . . . . . . . . . . . . .
50-54 . . . . . . . . . . . . . . . . . .
55-59 . . . . . . . . . . . . . . . . . .
60-62 . . . . . . . . . . . . . . . . . .
63-64 . . . . . . . . . . . . . . . . . .
65-67 . . . . . . . . . . . . . . . . . .
68-69 . . . . . . . . . . . . . . . . . .
70-74 . . . . . . . . . . . . . . . . . .
75+. . . . . . . . . . . . . . . . . . . .
Sources of Independent Controls
The independent population controls used in the secondstage ratio adjustment are prepared by projecting forward
the population figures derived from the 1990 decennial
census using information from a variety of other data
sources that account for births, deaths, and net migration.
Subtracting estimated numbers of resident Armed Forces
personnel and institutionalized persons from the resident
White
female
Ages
Other1 Other1
male female
0-5 . . . . . . . . . . . . . . . . . .
6-13 . . . . . . . . . . . . . . . . .
*14 . . . . . . . . . . . . . . . . . .
*15 . . . . . . . . . . . . . . . . . .
16-44. . . . . . . . . . . . . . . .
45+ . . . . . . . . . . . . . . . . .
*No distinction is made between sexes.
1
Other includesAmerican Indian, Eskimo,
Aleut, Asian, and Pacific Islander.
population gives the civilian noninstitutional population.
Estimates of net census undercount, determined from the
Post-Enumeration Survey, are added to the population
projections. One should note that, prepared in this manner,
the controls are themselves estimates. However, they are
derived independently of the CPS and provide useful
information for adjusting the sample estimates. See Appendix D for more details on sources and derivation of the
independent controls.
10–7
Computing Initial Second-Stage Ratio
Adjustment Factors
As mentioned before, the second-stage adjustment involves
a three-way rake:
These initial factors are not used in the estimates. They are
only used to determine whether any cells need to be
collapsed. A cell is combined with adjacent (next higher or
next lower) age cells in the same race/sex or ethnicity/sex
category if:
• State
• It contains no sample respondents (i.e., Ejk = 0).
• Hispanic/sex/age
• Its initial factor is less than or equal to 0.6.
• Race/sex/age
• Its initial factor is greater than or equal to 2.0.
In order to prevent the second-stage adjustment from
increasing the variance of the sample estimates, estimation cells which have zero estimates (i.e., no sample
respondents) or extremely large or small adjustment factors are identified and combined (or collapsed) with others.
No collapsing is done for the state rake. Prior to iteration 1
for the Hispanic/sex/age and the race/sex/age rakes, initial
adjustment factors are computed by rotation group for each
of the cells shown in Tables 10–2 and 10–3.
For any particular cell j, the initial factor is computed as:
Collapsing cells continues until none of the three criteria
are met or all available cells have been collapsed. Once
cells are collapsed, all cell definitions are maintained
through all six iterations of the raking procedure. In a
typical month approximately 10 cells require collapsing.
Fjk ⫽ Cj Ⲑ Ejk
where
Cj ⫽
the population control for cell j (divided by 8,
because the raking is done for each rotation
group)
Ejk ⫽
the CPS estimate for cell j and rotation group k
after the first-stage ratio adjustment.
Raking
For each iteration of each rake an adjustment factor is
computed for each cell and applied to the estimate of that
cell. The factor is the population control divided by the
estimate of the current iteration for the particular cell. The
three steps are repeated through six iterations. The following simplified example begins after one state rake. The
example shows the raking for two cells in an ethnicity rake
and two cells in a race rake. Age/sex cells and one race cell
(see Tables 10–2 and 10–3) have been collapsed here for
simplification.
10–8
Iteration 1:
State rake
Hispanic rake
Race rake
Example of Raking Ratio Adjustment
Raking Estimates by Ethnicity and Race
Es = Estimate from CPS sample after state rake
Ee = Estimate from CPS sample after ethnicity rake
Er = Estimate from CPS sample after race rake
Fe = Ratio adjustment factor for ethnicity
Fr = Ratio adjustment factor for race
Iteration 1 of the Ethnicity Rake
Non-Black
Black
Population
controls
Non-Hispanic
Hispanic
Population controls
Es ⫽ 650
1050
⫽ 1.265
Fe ⫽
650⫹180
Ee ⫽ EsFe ⫽ 822
Es=150
250
= 1.471
Fe=
150+20
Ee= EsFe= 221
1000
Es ⫽ 180
1050
⫽ 1.265
Fe ⫽
650⫹180
Ee ⫽ EsFe ⫽ 228
Es ⫽ 20
250
⫽ 1.471
Fe ⫽
150⫹20
Ee ⫽ EsFe ⫽ 29
300
1050
250
1300
Non-Hispanic
Hispanic
Population controls
Ee ⫽ 822
1000
⫽ .959
Fr ⫽
822⫹221
Er ⫽ EeFr ⫽ 788
Ee=221
1000
=.959
Fr=
822+221
Er ⫽ EeFr ⫽ 212
1000
Ee ⫽ 228
300
⫽ 1.167
Fr ⫽
228⫹29
Er ⫽ EeFr ⫽ 266
Ee ⫽ 29
300
⫽ 1.167
Fr ⫽
228⫹29
Er ⫽ EeFr ⫽ 34
300
1050
250
1300
Iteration 1 of the Race Rake
Non-Black
Black
Population
controls
Iteration 2 (repeat steps above beginning with sample cell
estimates at the end of iteration 1)
.
.
.
Iteration 6
Note that the matching of estimates to controls for the
race rake causes the cells to differ slightly from the controls
for the ethnicity rake or previous rake. With each rake,
these differences decrease when cells are matched to the
controls for the most recent rake. For the most part, after
six iterations the estimates for each cell have converged to
the population controls for each cell. Thus, the weight for
each record after the second-stage ratio adjustment procedure can be thought of as the weight for the record after
the first-stage ratio adjustment multiplied by a series of 18
adjustment factors (six iterations of three rakes). The
product of these 18 adjustment factors is called the secondstage ratio adjustment factor.
10–9
Weight After the Second-Stage Ratio Adjustment
At the completion of the second-stage ratio adjustment,
the record for each person has a weight reflecting the
product of:
(baseweight) x (special weighting adjustment factor) x
(noninterview adjustment factor) x
(first-stage ratio adjustment factor) x
(second-stage ratio adjustment factor)
The weight after the second-stage ratio adjustment is also
called the final weight. The estimates produced using the
final weights are often called the first- and second-stage
combined (FSC) estimates. The second-stage ratio adjustment factors also provide estimates of CPS coverage (see
Chapter 16).
greatest for estimates of month-to-month change, improvements are also realized for estimates of change over other
intervals of time and for estimates of levels in a given
month (Breau and Ernst, 1983).
Prior to 1985, the two estimators described in the
preceding paragraph were the only terms in the CPS
composite estimator and were given equal weight. Since
1985, the weights for the two estimators have been unequal
and a third term has been included, an estimate of the net
difference between the incoming and continuing parts of
the current month’s sample. The formula for the composite
estimate of a labor force level YtⲐ is
Ⲑ
⫹ 䉭t兲 ⫹ ⟨␤ˆ t
YtⲐ ⫽ 共1⫺K兲Ŷt ⫹ K共Yt⫺1
where
8
Ŷt ⫽
兺 xt,i
i⫽1
COMPOSITE ESTIMATOR2
Once each record has a final weight, an estimate of level
for any given set of characteristics identifiable in the CPS
can be computed by summing the final weights for all the
sample cases that have that set of characteristics. The
process for producing this type of estimate has been
variously referred to as a Horvitz-Thompson estimator, a
two-stage ratio estimator, or a simple weighted estimator.
But the estimator actually used for the derivation of most
official CPS labor force estimates that are based upon
information collected every month from the full sample (in
contrast to information collected in periodic supplements or
from partial samples) is a composite estimator. Composite
estimation for the CPS modifies the aggregated FSC
estimates without adjusting the weights of individual sample
records. This is a disadvantage for data users, since
composite estimates for a particular month cannot be
produced from a microdata file for only that month. However, research is being conducted into the possibility of
using a composite estimator from which composite estimates could be produced from a single month’s microdata
file (Lent, Miller, and Cantwell, 1994).
In general, a composite estimate is a weighted average
of several estimates. The composite estimate from the
CPS has historically combined two estimates. The first of
these is the FSC estimate described above. The second
consists of the composite estimate for the preceding month
and an estimate of the change from the preceding to the
current month. The estimate of the change is based upon
data from that part of the sample which is common to the
two months (about 75 percent). The higher month-to-month
correlation between estimates from the same sample units
tends to reduce the variance of the estimate of month-tomonth change. Although the average improvements in
variance from the use of the composite estimator are
2
See Appendix I for update.
䉭t ⫽
4
共xt,i ⫺ xt⫺1, i⫺1兲 and
3兺
i⑀s
␤ˆ t ⫽
i
=
xt,i ⫽
1
xt,i
兺 xt,i ⫺ 3兺
i僆s
i␧s
1,2,...,8 (month in sample).
sum of weights after second-stage ratio adjustment
of respondents in month t, and month in sample i
with characteristic of interest.
S = {2,3,4,6,7,8}.
In the formula above, Ŷt is the current month’s FSC
estimate, 䉭t is the estimate of change from rotation groups
common to months t and t-1, and ␤ˆ t is the estimate of the
net difference between the incoming and continuing part of
the current month’s sample. The third term, ␤ˆ t, was added
primarily because it was found to keep composited and
uncomposited estimates closer while leaving intact the
variance reduction advantages of the composite estimator.
It also is often characterized as an adjustment term for the
bias associated with rotation groups or time in sample in
the CPS (see Chapter 15). Historically, the incoming parts
of the sample (the rotation groups in their first or fifth
month-in-sample) have tended to have higher labor force
participation and unemployment rates than the continuing
parts (Bailar, 1975). The third term offsets, to some degree,
the effect of this bias on the estimate of change in the
second term. If there was no such bias, then the expected
value of ␤ˆ t would be zero.
The values of the constants K (K=0.4) and A (A=0.2)
were chosen to be approximately optimal for reducing
variances of estimates of labor force characteristics. They
reflect a compromise of optimal values across a variety of
characteristics and types of estimates (Kostanich and
Bettin, 1986).
10–10
The compositing formula is applied only to estimated
levels. Other types of composite estimates (rates, percents, means, medians) are computed from the component
composited levels (e.g., an unemployment rate is computed by taking the composited unemployment level as a
percentage of the composited labor force level).
SEASONAL ADJUSTMENT
A time series is a sequence of observations (measurements or estimates) of a particular measurable phenomenon over time. The use and interpretation of many time
series, including many of the monthly labor force time
series that are based on the CPS, are enhanced by the
process of seasonal adjustment. The objective of seasonal
adjustment is to measure and remove from time series the
effects of normal seasonality caused by such things as
weather, holidays, and school schedules. Seasonality can
account for much of the observed month-to-month change
in estimates, such as those for employment and unemployment, and can obscure the underlying movements associated with long-term cycles and trends that are of great
economic significance for most users. For example, the
unadjusted CPS levels of employment and unemployment
in June are consistently much higher than those for May
because of the influx of students into the labor force. If the
only change that occurred in the unadjusted estimates
between May and June approximated the normal seasonal
change, then the seasonally adjusted estimates for the 2
months should be about the same, indicating that essentially no change occurred in the underlying business cycle
and trend even though there may have been a large
change in the unadjusted data. Changes that do occur in
the seasonally adjusted series reflect changes not associated with normal seasonal change and should provide
information about the direction and magnitude of changes
in the behavior of trend and business cycle effects. They
may, however, also reflect the effects of sampling error and
other irregularities, which are not removed by the seasonal
adjustment process. Change in the seasonally adjusted
series can and occasionally should be in a direction
opposite the movement in the unadjusted series.
Refinements of the methods used for seasonal adjustment have been under development for decades. The
procedure used since 1980 for the seasonal adjustment of
the labor force series is the X-11 ARIMA program, developed by Statistics Canada in the late 1970s as an extension and improvement of the widely used X-11 method
developed at the U.S. Census Bureau in the 1960s. The
X-11 approach to seasonal adjustment is univariate and
nonparametric and involves the iterative application of a
set of moving averages that can be summarized as one
lengthy weighted average (Dagum, 1983). Nonlinearity is
introduced by a set of rules and procedures for identifying
and reducing the effect of ‘‘extreme values.’’ In most uses
of X-11 seasonal adjustment, including that for CPS-based
labor force series, the seasonality is estimated as evolving
rather than fixed over time. A detailed description of the
program is given in Scott and Smith (1974).
The current official practice for the seasonal adjustment
of the labor force series involves the running of all directly
adjusted series through X-11 ARIMA twice each year, after
receipt of June and December data, with 6 months of
projected factors drawn from each run and used in the
subsequent 6 months, and historical revisions drawn from
the end-of-year run. This practice allows, among other
things, the publication of the seasonal factors prior to their
use.
Seasonally adjusted estimates of many labor force
series, including the levels of the civilian labor force,
employment, and unemployment and all unemployment
rates, are derived indirectly by arithmetically combining or
aggregating the series directly adjusted with X-11 ARIMA.
For example, the overall unemployment rate is computed
using 12 directly adjusted components—unemployment,
agricultural employment, and nonagricultural employment
by sex and by age (16-19 and 20+). The principal reason
for doing such indirect adjustment is that it ensures that the
major seasonally adjusted totals will be arithmetically consistent with at least one set of components. If the totals
were directly adjusted along with the components, such
consistency would generally not occur because X-11 is not
a sum- or ratio-preserving procedure. It is not generally
appropriate to apply factors computed for an aggregate
series to the components of the aggregate because various components tend to have significantly different patterns of seasonal variation.
For up-to-date information and a more thorough discussion on the seasonal adjustment of the labor force series,
see the January issues of Employment and Earnings
(U.S. Department of Labor).
MONTHLY STATE AND SUBSTATE ESTIMATES
Employment and unemployment estimates for states
and local areas are key indicators of local economic
conditions. Under a Federal-state cooperative program,
monthly estimates of the civilian labor force and unemployment are prepared for some 6,950 areas, including all
states, metropolitan areas, counties, cities of 25,000 population or more, and all cities and towns in New England.
The Bureau of Labor Statistics is responsible for the
concepts, definitions, technical procedures, validation, and
publication of the estimates which are prepared by state
employment security agencies.
The state and area estimates are used by a wide variety
of customers. Federal programs base allocations to states
and areas on the data, as well as eligibility determinations
for assistance. State and local governments use the estimates for planning and budgetary purposes and to determine the need for local employment and training services.
Private industry and individuals use the data to compare
and assess labor market developments in states and
substate areas.
10–11
The underlying concepts and definitions of all labor force
data developed for state and substate areas are consistent
with those of the Current Population Survey (CPS). Annual
average estimates for all states are derived directly from
the CPS. In addition, monthly estimates for 11 large states
(California, Florida, Illinois, Massachusetts, Michigan, New
Jersey, New York, North Carolina, Ohio, Pennsylvania, and
Texas) and two substate areas (New York City and the Los
Angeles-Long Beach metropolitan area) also come directly
from the CPS; these areas have a sufficiently large sample
to meet the BLS standard of reliability. For the remaining 39
states and the District of Columbia, where the CPS sample
is not large enough to produce reliable monthly estimates,
data are produced using a signal-plus-noise time series
models. These models combine current and historical data
from the CPS, the Current Employment Statistics (CES)
program, and State unemployment insurance (UI) systems
to produce estimates that reflect each state’s individual
economy.
Estimates for substate labor market areas (other than
the two direct-use CPS areas) are produced through a
building block approach which uses data from several
sources, including the CPS, CES, state unemployment
insurance systems, and the decennial census to create
estimates which are then adjusted to the state modelbased measures of employment and unemployment. Below
the labor market area level, estimates are prepared for all
counties and cities with populations of 25,000 or more
using disaggregation techniques based on decennial census and annual population estimates and current UI statistics.
Estimates for States
The employment and unemployment estimates for 11
large states (California, Florida, Illinois, Massachusetts,
Michigan, New Jersey, New York, North Carolina, Ohio,
Pennsylvania, and Texas) are sufficiently reliable to be
taken directly from the Current Population Survey (CPS) on
a monthly basis. These states are termed ‘‘direct-use’’
states.
For the 39 smaller states and the District of Columbia
(called ‘‘nondirect-use’’ states) models based on a signalplus-noise approach (Bell and Hillmer, 1990; Tiller, 1992)
are used to develop employment and unemployment estimates.3 The model of the signal is a time series model of
the true labor force which consists of three components: a
variable coefficient regression, a flexible trend, and a
flexible seasonal component. The regression components
are based on historical and current relationships found
within each state’s economy as reflected in the different
sources of data that are available for each state—the CPS,
the Current Employment Statistics (CES) survey, and the
3
Because of the January 1996 sample reduction, estimates for all
states are based on a model.
unemployment insurance (UI) system. The noise component of the models explicitly accounts for autocorrelation in
the CPS sampling error and changes in the average
magnitude of the error. While all the state models have
important components in common, they differ somewhat
from one another to better reflect individual state labor
force characteristics. See Appendix E for more details on
the models.
Two models—one for the employment-to-population ratio
and one for the unemployment rate—are used for each
state. The employment-to-population ratio, rather than the
employment level, and the unemployment rate, rather than
the unemployment level, are estimated primarily because
the hyperparameters used in the state-space models are
easier to estimate and compare across states for ratios
than levels.
The employment-to-population ratio models estimate
the monthly CPS employment-to-population ratio using the
state’s monthly CPS data and employment data from the
CES. The models also include trend and seasonal components to account for different seasonal patterns in the CPS
not captured by the CES series. The seasonal component
accounts for the seasonality in the CPS not explained by
the CES while the trend component adjusts for long-run
systematic differences between the two series (Evans,
Tiller, and Zimmerman, 1993).
The unemployment rate models estimate the monthly
CPS unemployment rate using the state’s monthly unemployment insurance claims data, along with trend and
seasonal components (Zimmerman, Evans, and Tiller, 1993).
Once the estimates are developed from the models, levels
are calculated for the employment, unemployment, and
labor force.
State estimates are seasonally adjusted using the X-11
ARIMA seasonal-adjustment procedure twice a year and
new seasonal adjustment factors are created. The entire
series is used as an input to the seasonal adjustment
process; however, for programming purposes only the last
5 years of data are replaced.
Benchmarking and Population Controls
Once each year, when new population controls are
available from the Census Bureau, CPS labor force estimates for all states and the District of Columbia are
adjusted to these controls. The model-based estimates for
the 39 states and the District of Columbia are reestimated
incorporating the recontrolled CPS labor force estimates
(see above), and these (monthly) estimates are adjusted,
or benchmarked, to the newly population-controlled annual
average CPS estimates. The benchmarking technique
uses a procedure called the Denton Method (Denton,
1971) which adjusts the annual average of the modelbased employment and unemployment levels to equal the
corresponding CPS annual averages, while preserving, as
much as possible, the original monthly seasonal pattern of
the model-based estimates.
10–12
Substate estimates for previous years are also revised
on an annual basis. The updates incorporate any changes
in the inputs, such as revisions to establishment-based
employment estimates, corrections in claims data, and
updated historical relationships. The revised estimates are
then readjusted to the latest (benchmarked) state estimates of employment and unemployment.
PRODUCING OTHER LABOR FORCE
ESTIMATES
In addition to basic weighting to produce estimates for
persons, several ‘‘special-purpose’’ weighting procedures
are performed each month. These include:
• Weighting to produce estimates for households and
families.
• Weighting to produce estimates from data based on only
2 of 8 rotation groups (outgoing rotation weighting for the
quarter sample data).
• Weighting to produce labor force estimates for veterans
and nonveterans (veterans weighting).
• Weighting to produce estimates from longitudinally-linked
files (longitudinal weighting).
Most of these special weights are based on the final
weight after the second-stage ratio adjustment. Some also
make use of composited estimates. In addition, consecutive monthly estimates are often averaged to produce
quarterly or annual average estimates. Each of these
procedures is described in more detail below.
Family Weight
Family weights are used to produce statistics on families
and family composition. They also provide the basis for
household weights. The family weight is derived from the
final weight of the reference person in each household. In
most households, it is exactly the reference person’s
weight. However, when the reference person is a married
man, for purposes of family weights, he is given the same
weight as his wife. This is done so that weighted tabulations of CPS data by sex and marital status show an equal
number of married women and married men with their
spouses present. If the CPS final weights were used for
this tabulation (without any further adjustment), the estimated numbers of married women and married men would
not be equal, since the second-stage ratio adjustment
tends to increase the weights of males more than the
weights of females. The wife’s weight is usually used as the
family weight, since CPS coverage ratios for women tend
to be higher and subject to less month-to-month variability
than those for men.
Household Weight
The same household weight is assigned to every person
in the same household and is equal to the family weight of
the household reference person. The household weight is
used to produce estimates at the household level, such as
the number of households headed by a female or the
number of occupied housing units.
Outgoing Rotation Weights (Quarter Sample
Data)
Some items in the CPS questionnaire are asked only in
households due to rotate out of the sample temporarily or
permanently after the current month. These are the households in the rotation groups in their fourth or eighth monthin-sample, sometimes referred to as the ‘‘outgoing’’ rotation
groups. Items asked in the outgoing rotations include those
on discouraged workers (through 1993), earnings (since
1979), union affiliation (since 1983), and industry and
occupation of second jobs of multiple jobholders (beginning in 1994). Since the data are collected from only
one-fourth of the sample each month, these estimates are
averaged over 3 months to improve their reliability, and
published quarterly.
Since 1979, most CPS files have included separate
weights for the outgoing rotations. These weights were
generally referred to as ‘‘earnings weights’’ on files through
1993, and are generally called ‘‘outgoing rotation weights’’
on files for 1994 and subsequent years. In addition to ratio
adjustment to independent population controls (in the
second stage), these weights also reflect additional constraints that force them to sum to the composited estimates
of employment, unemployment, and not in labor force each
month. An individual’s outgoing rotation weight will be
approximately four times his or her final weight.
To compute the outgoing rotation adjustment factors, the
final CPS weights of the appropriate records in the two
outgoing rotation groups are tallied. CPS composited estimates from the full sample for the labor force categories of
employed wage and salary workers, other employed, unemployed, and not in labor force by age, race and sex are
used as the controls. The adjustment factor for a particular
cell is the ratio of the control total to the weighted tally from
the outgoing rotation groups.
Collapsing is performed with an adjacent cell (the next
higher or lower age group) if:
• A cell has no sample respondents,
• A composited control total is less than or equal to zero, or
•
The corresponding adjustment factor is less than or
equal to 2.0 or greater than or equal to 8.0 (i.e., one-half
or twice the normal adjustment factor of 4).
If a cell requires collapsing, it is collapsed with another
cell (the next higher or lower age group) in the same sex,
race, and labor force category. The adjustment factors are
10–13
recomputed after all collapsing has been performed. The
outgoing rotation weights are obtained by multiplying the
outgoing ratio adjustment factors by the final CPS weights.
For consistency, an outgoing rotation group weight equal to
four times the basic CPS family weight is assigned to all
persons in the two outgoing rotation groups who were not
eligible for this special weighting (military personnel and
persons aged 15 and younger).
Production of monthly, quarterly, and annual estimates
using the quarter sample data and the associated weights
is completely parallel to production of uncomposited, simple
weighted estimates from the full sample—the weights are
summed and divided by the number of months used. The
composite estimator is not applicable for these estimates
because there is no overlap between the quarter samples
in consecutive months. Because the outgoing rotations are
all independent samples within any consecutive 12-month
period, averaging of these estimates on a quarterly and
annual basis realizes relative reductions in variance greater
than those achieved by averaging full sample estimates.
Family Outgoing Rotation Weight
The family outgoing rotation weight is analogous to the
family weight computed for the full sample, except that
outgoing rotation weights are used, rather than the final
weights from the second-stage ratio adjustment.
Veterans’ Weights
Since 1986, CPS interviewers have collected data on
veteran status from all respondents. Veterans’ weights are
calculated for all CPS respondents based on their veteran
status. This information is used to produce tabulations of
employment status for veterans and nonveterans.
The process begins with the final weights after the
second-stage ratio adjustment. Each respondent is classified as a veteran or a nonveteran. Veterans’ records are
classified into six cells: (1) male, pre-Vietnam veteran; (2)
female, pre-Vietnam veteran; (3) male, Vietnam veteran;
(4) female, Vietnam veteran; (5) male, other veteran; and
(6) female, other veteran. The cell definitions change
throughout the decade as the minimum age for nonpeacetime veterans increases.
The final weights for CPS veterans are tallied into
sex/age/type-of-veteran cells using the classifications described
above. Separate ratio adjustment factors are computed for
each cell, using independently established monthly counts
of veterans provided by the Department of Veterans Affairs.
The ratio adjustment factor is the ratio of the independent
control total to the sample estimate. The final weight for
each veteran is multiplied by the appropriate adjustment
factor to produce the veteran’s weight.
To compute veterans’ weights for nonveterans, a table of
composited estimates is produced from the CPS data by
sex, race (White/non-White), labor force status (unemployed, employed, and not in the labor force), and age. The
veterans’ weights produced in the previous step are tallied
into the same cells. The estimated number of veterans is
then subtracted from the corresponding cell entry for the
composited table to produce nonveterans control totals.
The final weights for CPS nonveterans are tallied into the
same sex/race/labor force status/age cells. Separate ratio
adjustment factors are computed for each cell, using the
nonveterans controls derived above. The factor is the ratio
of the nonveterans control total to the sample estimate. The
final weight for each nonveteran is multiplied by the appropriate factor to produce the nonveterans’ weight. A table of
labor force estimates by age status for veterans and
nonveterans is published each month.
Longitudinal Weights
For many years, the month-to-month overlap of 75
percent of the sample households has been used as the
basis for estimating monthly ‘‘gross flow’’ statistics. The
difference or change between consecutive months for any
given level or ‘‘stock’’ estimate is an estimate of net change
that reflects a combination of underlying flows in and out of
the group represented. For example, the month-to-month
change in the employment level is the number of people
who went from not being employed in the first month to
being employed in the second month minus the number
who made the opposite transition. The gross flow statistics
provide estimates of these underlying flows and can provide useful insights to analysts beyond those available in
the stock data.
The estimation of monthly gross flows, and any other
longitudinal use of the CPS, begins with a longitudinal
matching of the microdata (or person-level) records within
the rotation groups common to the months of interest. Each
matched record brings together all the information collected in those months for a particular individual. The CPS
matching procedure uses the household identifier and
person line number as the keys for matching. Prior to 1994,
it was also necessary to check other information and
characteristics, such as age and sex, for consistency to
verify that the match based on the keys was almost
certainly a valid match. Beginning with 1994 data, the
simple match on the keys provides an essentially certain
match. Because the CPS does not follow movers (rather,
the sample addresses remain in sample according to
rotation pattern), and because not all households are
successfully interviewed every month they are in sample, it
is not possible to match interview information for all persons in the common rotation groups across the months of
interest. The highest percentage of matching success is
generally achieved in the matching of consecutive months,
where between 90 and 95 percent of the potentially matchable records (or about 67 to 71 percent of the full sample)
can usually be matched. The use of CATI and CAPI since
1994 has also introduced dependent interviewing which
eliminated much of the erratic differences in response
between pairs of months.
10–14
On most CPS files for 1994 forward, there is a longitudinal weight which allows users to estimate gross labor
force flows by simply summing up the longitudinal weights
after matching. These longitudinal weights reflect the technique that had been used prior to 1994 to inflate the gross
flow estimates to appropriate population levels. That technique simply inflates all estimates or final weights by the
ratio of the current month’s population controls to the sum
of the final weights for the current month in the matched
cases by sex. Although the technique does provide estimates consistent with the population levels for the stock
data in the current month, it does not force consistency with
labor force stock levels in either the current or the previous
month, nor does it control for the effects of the bias and
sample variation associated with the exclusion of movers,
differential noninterview in the matched months, the potential for the compounding of classification errors in flow data,
and the particular rotations that are common to the matched
months. There have been a number of proposals for
improving the estimation of gross labor force flows, but
none have yet been adopted in official practice. See
Proceedings of the Conference on Gross Flows in Labor
Force Statistics (U.S. Department of Commerce and U.S.
Department of Labor, 1985) for information on some of
these proposals and for more complete information on
gross labor force flow data and longitudinal uses of the
CPS.
Averaging Monthly Estimates
CPS estimates are frequently averaged over a number
of months. The most commonly computed averages are (1)
quarterly, which provide four estimates per year by grouping the months of the calendar year in nonoverlapping
intervals of three, and (2) annual, combining all 12 months
of the calendar year. Quarterly and annual averages of
uncomposited data can be computed by summing the
weights for all of the months contributing to each average
and dividing by the number of months involved. Averages
for calculated cells, such as rates, percents, means, and
medians, are computed from the averages for the component levels, not by averaging the monthly values (e.g., a
quarterly average unemployment rate is computed by
taking the quarterly average unemployment level as a
percentage of the quarterly average labor force level, not
by averaging the three monthly unemployment rates together).
Although such averaging multiplies the number of interviews contributing to the resulting estimates by a factor
approximately equal to the number of months involved in
the average, the sampling variance for the average estimate is actually reduced by a factor substantially less than
that number of months. This is primarily because the CPS
rotation pattern and resulting month-to-month overlap in
sample units ensure that estimates from the individual
months are not independent. The reduction in sampling
error associated with the averaging of CPS estimates over
adjacent months was studied using 12 months of data
collected beginning January 1987 (Fisher and McGuinness, 1993). That study showed that characteristics for
which the month-to-month correlation is low, such as
unemployment, are helped considerably by such averaging, while characteristics for which the correlation is high,
such as employment, benefit less from averaging. For
unemployment, variances of national estimates were reduced
by about one-half for quarterly averages and about onefifth for annual averages.
REFERENCES
Bailar, B., (1975) ‘‘The Effects of Rotation Group Bias on
Estimates from Panel Surveys,’’ Journal of the American
Statistical Association, Vol. 70, pp. 23-30.
Bell, W.R., and Hillmer, S.C. (1990), ‘‘The Time Series
Approach to Estimation for Repeated Surveys,’’ Survey
Methodology, 16, pp. 195-215.
Breau, P. and Ernst, L., (1983) ‘‘Alternative Estimators to
the Current Composite Estimator,’’ Proceedings of the
Section on Survey Research Methods, American Statistical Association, pp. 397-402.
Copeland, K. R., Peitzmeier, F. K., and Hoy, C. E.
(1986), ‘‘An Alternative Method of Controlling Current Population Survey Estimates to Population Counts,’’ Proceedings of the Survey Research Methods Section, American Statistical Association, pp.332-339.
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Denton, F.T. (1971), ‘‘Adjustment of Monthly or Quarterly
Series to Annual Totals: An Approach Based on Quadratic
Minimization,’’ Journal of the American Statistical Association, 64, pp. 99-102.
Evans, T.D., Tiller, R.B., and Zimmerman, T.S. (1993),
‘‘Time Series Models for State Labor Force Estimates,’’
Proceedings of the Survey Research Methods Section,
American Statistical Association, pp. 358-363.
Fisher, R. and McGuinness R. (1993), ‘‘Correlations and
Adjustment Factors for CPS (VAR80 1).’’ Internal Memorandum for Documentation, January 6th, Demographic
Statistical Methods Division, U.S. Census Bureau.
Hansen, M. H., Hurwitz, W. N., and Madow, W. G.(1953),
Sample Survey Methods and Theory, Vol. II, New York:
John Wiley and Sons.
Harvey, A.C. (1989), Forecasting, Structural Time
Series Models and the Kalman Filter, Cambridge: Cambridge University Press.
Ireland, C.T., and Kullback, S. (1968), ‘‘Contingency
Tables With Given Marginals,’’ Biometrika, 55, pp. 179187.
Kostanich, D. and Bettin, P. (1986), ‘‘Choosing a Composite Estimator for CPS,’’ presented for presentation at
the International Symposium on Panel Surveys, Washington, DC.
10–15
Lent, J., Miller, S., and Cantwell, P. (1994), ‘‘Composite
Weights for the Current Population Survey,’’ Proceedings
of the Survey Research Methods Section, American
Statistical Association, pp. 867-872.
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Application Issues in Raking Estimation,’’ Proceedings of
the Section on Survey Research Methods, American
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Scott, J.J., and Smith, T.M.F. (1974), ‘‘Analysis of Repeated
Surveys Using Time Series Methods,’’ Journal of the
American Statistical Association, 69, pp. 674678.
Thompson, J.H., (1981), ‘‘Convergence Properties of
the Iterative 1980 Census Estimator,’’ Proceedings of the
American Statistical Association on Survey Research
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Tiller, R.B. (1989), ‘‘A Kalman Filter Approach to Labor
Force Estimation Using Survey Data,’’ Proceedings of the
Survey Research Methods Section, American Statistical
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‘‘State Unemployment Rate Time Series,’’ Proceedings of
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11–1
Chapter 11.
Current Population Survey Supplemental Inquiries
INTRODUCTION
In addition to providing data on the labor force status of
the population, the Current Population Survey (CPS) is
used to collect data for a variety of studies on the entire
U.S. population and specific population subsets. Such
studies keep the nation informed of the economic and
social well being of its people and are conducted for use by
Federal and state agencies, private foundations, and other
organizations. Supplemental inquiries take advantage of
several special features of the CPS: large sample size and
general purpose design; highly skilled, experienced interviewing and field staff; and generalized processing systems that can easily accommodate the inclusion of additional questions.
Some CPS supplemental inquiries are conducted annually, others every other year, and still others on a one-time
basis. The frequency and recurrence of a supplement
depend on what best meets the needs of the supplement’s
sponsor. It is important that any supplemental inquiry meet
strict criteria discussed in the next section.
Producing supplemental data from the CPS involves
more than just including additional questions. Separate
data processing is required to edit responses for consistency and to impute missing values. Another weighting
method is often necessary because the supplement targets
a different universe from the basic CPS. A supplement can
also engender a different level of response or cooperation
from respondents.
CRITERIA FOR SUPPLEMENT INQUIRIES
As a basis for undertaking supplements for federal
agencies or other sponsors, a number of criteria which
determine the acceptability of such work have been developed and refined over the years. These criteria were
developed by the Census Bureau in consultation with the
Bureau of Labor Statistics (BLS).
The staff of the Census Bureau, working with the
sponsoring agency, develops the survey design, including
the methodology, questionnaires, pretesting options, interviewer instructions and processing requirements. The Census Bureau provides a written description of the statistical
properties associated with each supplement. The same
standards of quality that apply to the basic CPS apply to
the supplements.
The following criteria are considered before undertaking
a supplement:
1. The subject matter of the inquiry must be in the public
interest.
2. The inquiry must not have an adverse effect on the
CPS or other Census Bureau programs. The questions
must not cause respondents to question the importance of the survey and result in losses of response or
quality. It is essential that the image of the Census
Bureau as the objective fact finder for the Nation is not
damaged. Other important functions of the Census
Bureau, such as the decennial censuses or the economic censuses, must not be affected in terms of
quality or response rates or in congressional acceptance and approval of these programs.
3. The subject matter must be compatible with the basic
CPS survey. This is to avoid introducing a concept that
could affect the accuracy of responses to the basic
CPS information. For example, a series of questions
incorporating a revised labor force concept that could
inadvertently affect responses to the standard labor
force items would not be included.
4. The inquiry must not slow down the work of the basic
survey or impose a response burden that may affect
future participation in the basic CPS. In general, the
supplemental inquiry must not add more than 10
minutes of interview time per respondent or 25 minutes per household. All conflicts or competition for the
use of Census Bureau staff or facilities that arise in
dealing with the supplemental inquiry are resolved by
giving the basic CPS first priority. The Census Bureau
will not jeopardize the schedule for completing CPS or
other Census Bureau work to favor completing a
supplemental inquiry within some specified time frame.
5. The subject matter must not be overly sensitive. This
criterion is imprecise, and its interpretation has changed
over time. For example, the subject of birth expectations, once considered sensitive, has been included as
a CPS supplemental inquiry.
6. It must be possible to meet the objectives of the inquiry
through the survey method. That is, it must be possible
to translate the survey objectives into meaningful
questions, and the respondent must be able to supply
the information required to answer the questions.
11–2
7. If the supplemental information is to be collected
during the CPS interview, the inquiry must be suitable
for the personal visit/telephone procedures used in the
CPS.
8. All data must abide by the Census Bureau’s enabling
legislation, which, in part, ensures that no information
will be released that can identify an individual. Requests
for name, address, social security number, or other
information that can directly identify an individual will
not be included. In addition, information that could be
used to indirectly identify an individual with a high
probability of success (e.g., small geographic areas in
conjunction with income or age) will be suppressed.
9. The cost of supplements must be borne by the sponsor, regardless of the nature of the request or the
relationship of the sponsor to the ongoing CPS.
The questionnaires developed for the supplement are
subject to the Census Bureau’s pretesting policy. This
policy was established in conjunction with other sponsoring
agencies to encourage questionnaire research aimed at
improving data quality.
Even though the proposed inquiry is compatible with the
criteria given in this section, the Census Bureau does not
make the final decision regarding the appropriateness or
desirability of the supplemental survey. The Office of
Management and Budget, through its Statistical Policy
Division, reviews the proposal to make certain it meets
government wide standards regarding the need for the
data and appropriateness of the design and ensures that
the survey instruments, strategy, and response burden are
acceptable.
RECENT SUPPLEMENTAL INQUIRIES
The scope and type of CPS supplemental inquiries vary
considerably from month to month and from year to year.
Generally, in any given month, a respondent who is selected
for the supplement is asked the additional questions that
comprise the supplemental inquiry after completing the
regular part of the CPS. Table 11-1 summarizes CPS
supplemental inquiries that were conducted between September 1994 and December 2001.
The Housing Vacancy Supplement (HVS) is unusual in
that it is the only supplement that is conducted every
month. This supplement collects additional information
(e.g., number of rooms, plumbing, and rental/sales price)
on housing units identified as vacant in the basic CPS.
Probably the most widely used supplement is the Annual
Demographic Survey (ADS) which is conducted every
March. This supplement collects data on work experience,
several sources of income, migration, household composition, health insurance coverage, and receipt of noncash
benefits.
The basic CPS weighting is not always appropriate for
supplements, since supplements tend to have higher nonresponse rates. In addition, supplement universes are
generally different from the basic CPS universe. Thus,
some supplements require weighting procedures different
from the basic CPS. These variations are described for two
of the major supplements, the Housing Vacancy Survey
and the Annual Demographic Survey, in the following
sections.
Housing Vacancy Survey Supplement
Description of supplement
The HVS is a monthly supplement to the CPS sponsored by the Census Bureau. The supplement is administered when the CPS encounters a unit in sample that is
intended for year-round or seasonal occupancy and is
currently vacant, or occupied by persons with a usual
residence elsewhere. The interviewer asks a reliable respondent (e.g., the owner, a rental agent, or a knowledgeable
neighbor) questions on year built; number of rooms, bedrooms, and bathrooms; how long the housing unit has been
vacant; the vacancy status (for rent, for sale, etc); and
when applicable, the selling price or rent amount.
The purpose of the HVS is to provide current information
on the rental and homeowner vacancy rates, home ownership rates, and characteristics of units available for
occupancy in the United States as a whole, geographic
regions, and inside and outside metropolitan areas. The
rental vacancy rate is a component of the index of leading
economic indicators which is used to gauge the current
economic climate. Although the survey is performed monthly,
data for the nation and for Northeast, South, Midwest, and
West regions are released quarterly and annually. The data
released annually include information for states and large
metropolitan areas.
Calculation of vacancy rates
The HVS collects data on year-round and seasonal
vacant units. Vacant year-round units are those intended
for occupancy at any time of the year, even though they
may not be in use year-round. In resort areas, a housing
unit which is intended for occupancy on a year-round basis
is considered a year-round unit; those intended for occupancy only during certain seasons of the year are considered seasonal. Also, vacant housing units held for occupancy by migratory workers employed in farm work during
the crop season are classified as seasonal. The rental and
homeowner vacancy rates are the most prominent HVS
statistics. The vacancy rates are determined using information collected by the HVS and CPS since the formulas
use both vacant and occupied housing units.
The rental vacancy rate is calculated as the ratio of
vacant year-round units for rent to the sum of renter
occupied units, vacant year-round units rented but awaiting
occupancy, and vacant year-round units for rent.
11–3
Table 11–1. Current Population Survey Supplements September 1994 - December 2001
Title
Month
Purpose
Sponsor
Housing Vacancy
Monthly
Provide quarterly data on vacancy rates and characteristics of vacant Census
units.
Health/Pension
September 1994
Provide information on health/pension coverage for persons 40 years PWBA
of age and older. Information includes benefit coverage by former as
well as current employer and reasons for noncoverage, as appropriate. Amount, cost, employer contribution, and duration of benefits are
also measured. Periodicity: As requested.
Lead Paint Hazards
Awareness
December 1994
June 1997
Provide information on the current awareness of the health hazards HUD
associated with lead-based paint. Periodicity: As requested.
Contingent Workers
February 1995, 1997, 1999, Provide information on the type of employment arrangement workers BLS
2001
have on their current job and other characteristics of the current job such
as earnings, benefits, longevity, etc., along with their satisfaction with
and expectations for their current jobs. Periodicity: Biennial.
Annual Demographic
Supplement
March 1995 - 2001
Food Security
April 1995, September 1996, Data that will measure hunger and food security. It will provide data on FNS
April 1997, August 1998, April food expenditures, access to food, and food quality and safety.
1999, September 2000, April
2001, December 2001
Race and Ethnicity
May 1995, July 2000
Use alternative measurement methods to evaluate how best to collect BLS/Census
these types of data.
Marital History
June 1995
Information from ever married persons on marital history. Periodicity: Quin- Census/BLS
quennial.
Fertility
June 1995, 1998, 2000
Data on the number of children that women aged 15-44 have ever had Census/BLS
and the children’s characteristics. Periodicity: Quinquennial.
Educational Attainment
July 1995
Tested several methods of collecting these data. Tested both the cur- BLS/Census
rent method (highest grade completed or degree received) and the old
method (highest grade attended and grade completed).
Veterans
August 1995, August 1997, Data for veterans of the United States on Vietnam-theatre status, service- BLS
September 1999, August 2001 connected income, effect of a service-connected disability on current
labor force participation, and participation in veterans’ programs. Periodicity: Biennial.
School Enrollment
October 1994, 1995, 1996, Provide information on school enrollment, junior or regular college atten- BLS/Census/NCES
1997, 1998, 1999, 2000, 2001 dance, and high school graduation. Periodicity: Annual.
Tobacco Use
September 1995, January 1996,
May 1996, September 1998,
January 1999, May 1999, January 2000, May 2000, June
2001, November 2001
Data for 15+ population on current and former use of tobacco products; NCI
restrictions of smoking in workplace for employed persons; and personal
attitudes toward smoking; a repeat of supplements in 1995/1996. Supplements will be repeated in 1998/1999. Periodicity: As requested.
Displaced Workers
February 1996, 1998, 2000
Provide data on workers who lost a job in the last 5 years because of BLS
plant closing, shift elimination, or other work-related reason. Periodicity: Biennial.
Job Tenure/Occupational
Mobility
February 1996, 1998, 2000
Collect data that will measure an individual’s tenure with his/her current BLS
employer and in his/her current occupation. Periodicity: As requested.
Child Support
April 1996, 1998, 2000
Identify households with absent parents and provide data on child sup- OCSE
port arrangements, visitation rights of absent parent, amount and frequency of actual versus awarded child support, and health insurance
coverage. Data are also provided on why child support was not received
and/or awarded. April data will be matched to March data.
Voting and Registration
November 1994, 1996, 1998, Provide demographic information on persons who registered and did not Census
2000
register to vote. Also measures number of persons who voted and reasons for not registering. Periodicity: Biennial.
Work Schedule/
Home-Based Work
May 1997, 2001
Provide information about multiple job holdings and work schedules and BLS
telecommuters who work at a specific remote site.
Telephone Availability
March, July & November
1994-2001
Collect data on whether there is a phone in the HU, on which to con- FCC
tact the person; is a telephone interview acceptable.
Computer Use/Internet
Use
August 2000, September 2001, Obtain information about household access to computers and the use NTIA
November 1994, October 1997 of the internet or worldwide web.
Data concerning work experience, several sources of income, migra- Census/BLS
tion, household composition, health insurance coverage, and receipt of
noncash benefits. Periodicity: Annual.
11–4
The homeowner vacancy rate is calculated as the ratio
of vacant year-round units for sale to the sum of owner
occupied units, vacant year-round units sold but awaiting
occupancy, and vacant year-round units for sale.
Weighting procedure
Since the HVS universe differs from the CPS universe,
the HVS records require a different weighting procedure
from the CPS records. The HVS records are weighted by
the CPS basic weight, the CPS special weighting factor,
and two HVS adjustments. (Refer to Chapter 10 for a
description of the two CPS weighting adjustments.) The
two HVS adjustments are referred to as the HVS first-stage
ratio adjustment and the HVS second-stage ratio adjustment.
The HVS first-stage ratio adjustment is comparable to
the CPS first-stage ratio adjustment in that it reduces the
contribution to variance from the sampling of PSUs. The
adjustment factors are based on 1990 census data. For
each state, they are calculated as the ratio of the statelevel census count of vacant year-round housing units in all
NSR PSUs to the state-level estimate, using census counts,
of vacant year-round housing units in NSR PSUs.
The first-stage adjustment factors are applied to both
vacant year-round and seasonal housing units in NSR
PSUs.
The HVS second-stage ratio adjustment, which applies
to vacant year-round and seasonal housing units in SR and
NSR PSUs, is calculated as the ratio of the weighted CPS
interviewed housing units after CPS second-stage ratio
adjustment to the weighted CPS interviewed housing units
after CPS first-stage ratio adjustment.
The cells for the HVS second-stage adjustment are
calculated within each month-in-sample by census region
and type of area (metropolitan/nonmetropolitan, central
city/balance of MSA, and urban/rural). This adjustment is
made to all eligible HVS records.
The final weight for each HVS record is determined by
calculating the product of the CPS basic weight, the CPS
special weighting factor, the HVS first-stage ratio adjustment, and the HVS second-stage ratio adjustment. Note
that the occupied units in the denominator of the vacancy
rate formulas use a different final weight since the data
come from the CPS. The final weight applied to the renter
and owner-occupied units is the CPS household weight.
(Refer to Chapter 10 for a description of the CPS household weight.)
Annual Demographic Supplement (March
Supplement)
Description of supplement
The ADS is sponsored by the Census Bureau and the
Bureau of Labor Statistics (BLS). The Census Bureau has
collected data in the ADS since 1947. From 1947 to 1955,
the ADS took place in April and from 1956 to 2001 the ADS
took place in March. In 2002, a sample increase was
implemented requiring more time for data collection. Thus,
additional ADS interviews are now taking place in February
and April. However, even with this sample increase, most
of the data collection still occurs in March.
The supplement collects data on family characteristics,
household composition, marital status, migration, income
from all sources, information on weeks worked, time spent
looking for work or on layoff from a job, occupation and
industry classification of the job held longest during the
year, health insurance coverage, and receipt of noncash
benefits. A major reason for conducting the ADS around the
month of March is to obtain better income data. It was
thought that since March is the month before the deadline
for filing federal income tax returns, respondents were
likely to have recently prepared tax returns or be in the
midst of preparing such returns and could report their
income more accurately than at any other time of the year.
The ADS sample consists of the March CPS sample,
plus additional CPS households identified in the prior
November and following April CPS samples. Starting in
2002, the eligible ADS sample households are:
1. The entire March CPS sample.
2. Hispanic households — identified in November (from
all month-in-sample (MIS) rotation groups) and in April
(MIS 1,5).
3. Non-Hispanic non-White households — identified in
November (MIS 1, 5-8) and April (MIS 1,5).
4. Non-Hispanic White households with children 18 years
or younger — identified in November (MIS 1, 5-8) and
April (MIS 1,5).
Prior to 2002, only the November CPS households
containing at least one person of Hispanic origin were
added to the ADS. The added households in 2002, along
with a general sample increase in selected states (see
Appendix J), are collectively known as the State Children’s
Health Insurance Program (SCHIP) sample expansion.
The added households improve the reliability of the ADS
estimates for the Hispanic households, non-Hispanic nonWhite households, and non-Hispanic White households
with children 18 years or younger.
Because of the characteristics of CPS sample rotation
(see Chapter 3), the additional sample from the November
and April CPS rotation groups are completely different from
those in the March CPS. The additional sample cases
increase the effective sample size of the ADS compared to
the March CPS sample alone. The ADS sample includes
18 MIS rotation groups for Hispanic households, 15 MIS
rotation groups for non-Hispanic non-White households, 15
MIS rotation groups for non-Hispanic White households
with children 18 years or younger, and 8 MIS rotation
groups for all other households.
11–5
Table 11-2. Summary of ADS Interview Month
CPS month/Hispanic status
1
November
Hispanic
Hispanic
Month in sample
Month in sample
3
4
5
6
7
8
1
2
3
4
5
6
7
8
March
NI2
3
Feb.
NI
2
Feb.
Feb./Apr.
1
March
Non-Hispanic3
Hispanic1
April
Nonmover
1
Non-Hispanic
March
2
Mover
Non-Hispanic3
Apr.
NI2
Apr.
March
NI2
Apr.
NI2
Apr.
NI2
1
Hispanics may be of any race.
NI - Not interviewed for the ADS.
3
The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children ≤ 18 years old.
2
The March and April ADS eligible cases are administered the ADS questionnaire in those respective months
(see Table 11-2). The April cases are classified as ‘‘split
path’’ cases because they receive the ADS, while other
households receive the supplement scheduled for April.
The November eligible Hispanic households are administered the ADS questionnaire in March, regardless of rotation group (MIS 1-8). (Note that November MIS 5-8 households have already completed all 8 months of interviewing
for the CPS before March, and the November MIS 1-4
households have an extra contact scheduled for the ADS
before the 5th interview of the CPS later in the year.)
The November eligible non-Hispanic households (in MIS
1, 5-8) are administered the ADS questionnaire in either
February or April. November ADS eligible cases in MIS 1
and 5 are interviewed for the CPS in February (in MIS 4
and 8, respectively) so the ADS questionnaire is administered in February. (These are also split path cases, since
other households in the rotation groups get the regular
supplement scheduled for February). The November MIS
6-8 eligible cases are split between the February and April
CPS interviewing months.
Mover households, defined as households with a different reference person when compared to the November
CPS interview, are found at the time of the ADS interview.
Mover households identified from the November eligible
sample are removed from the ADS sample. Mover households identified in the March and April eligible samples
receive the ADS questionnaire.
The ADS sample universe is slightly different from the
CPS. The CPS completely excludes military personnel
while the ADS includes military personal who live in households with at least one other civilian adult. These differences require the ADS to have a different weighting
procedure from the regular CPS.
Weighting procedure
Prior to weighting, missing supplement items are assigned
values based on hot deck imputation, a system that uses a
statistical matching process. Values are imputed even
when all the supplement data are missing. Thus, there is no
separate adjustment for households that respond to the
basic survey but not to the supplement. The ADS records
are weighted by the CPS basic weight, the CPS special
weighting factor, the CPS noninterview adjustment, the
CPS first-stage ratio adjustment, and the CPS secondstage ratio adjustment procedure. (Refer to Chapter 10 for
a description of these adjustments.) Also there is an
additional ADS noninterview adjustment for the November
ADS sample, a SCHIP Adjustment Factor, a family equalization adjustment, and an adjustment for Armed Forces
members.
The November eligible sample, the March eligible sample,
and the April eligible sample are weighted separately up to
the second-stage weighting adjustment. The samples are
then combined so that one second-stage adjustment procedure is performed. The flowchart in Figure 11-1 illustrates the weighting process for the ADS sample.
Households from November eligible sample. The households from the November eligible sample start with their
basic CPS weight as calculated in November, modified by
their November CPS special weighting factor and November CPS noninterview adjustment. At this point, a second
noninterview adjustment is made for those November
eligible households that are still occupied, but an interview
could not be obtained in the February, March, or April CPS.
Then, the November ADS sample weights are adjusted by
the CPS first-stage adjustment ratio and finally, the weights
are adjusted by the SCHIP Adjustment Factor.
The ADS noninterview adjustment for November eligible
sample. The second noninterview adjustment is applied to
the November eligible sample households to reflect noninterviews of occupied housing units that occur in the February, March, or April CPS. If a noninterviewed household
is actually a Mover household, it would not be eligible for
interview. Since the mover status of noninterviewed households is not known, we assume that the proportion of
11–6
11–7
mover households is the same for interviewed and noninterviewed households. This is reflected in the noninterview
adjustment. With this exception, the noninterview adjustment procedure is the same as described in Chapter 10.
The weights of the interviewed households are adjusted by
the noninterview factor as described below. At this point,
the noninterviews and those November Mover households
are removed from any further weighting of the ADS. The
noninterview adjustment factor, Fij, is computed as follows:
Fij =
where:
Zij =
Nij =
Bij =
Zij + Nij + Bij
Zij + Bij
the weighted number of November eligible
sample households interviewed in the February, March, or April CPS in cell j of cluster
i.
the weighted number of November eligible
sample occupied, noninterviewed housing
units in the February, March, or April CPS in
cell j of cluster i.
the weighted number of November eligible
sample Mover households identified in the
February, March, or April CPS in cell j of
cluster i.
The weighted counts used in this formula are those after
the November CPS noninterview adjustment is applied.
The clusters refer to the variously defined regions that
compose the United States. These include clusters for the
Northeast, Midwest, South, and West, as well as clusters
for particular cities or smaller areas. Within each of these
clusters is a pair of residence cells. These could be (1)
Central City and balance of MSA, (2) MSA and non-MSA,
or (3) Urban and Rural, depending on the type of cluster.
SCHIP adjustment factor for the November eligible sample.
The SCHIP adjustment factor is applied to Nonmover
eligible households that contain residents who are Hispanic, non-Hispanic non-White, and non-Hispanic Whites
with children 18 years or younger to compensate for the
increased sample in these demographic categories. Hispanic households receive a SCHIP adjustment factor of
8/18 and non-Hispanic non-White households and nonHispanic White households with children 18 years or
younger receive a SCHIP adjustment factor of 8/15. (See
Table 11-3.) After this adjustment is applied, the November
ADS sample is ready to be combined with the March and
April eligible samples for the application of the secondstage ratio adjustment.
Households from April eligible sample. The households
from the April eligible sample start with their basic CPS
weight as calculated in April, modified by their April CPS
special weighting factor, the April CPS noninterview adjustment, and the SCHIP adjustment factor. After the SCHIP
adjustment factor is applied, the April eligible sample is
ready to be combined with the November and March
eligible samples for the application of the second-stage
ratio adjustment.
SCHIP adjustment factor for the April eligible sample. The
SCHIP adjustment factor is applied to April eligible households that contain residents who are Hispanic, non-Hispanic
non-Whites, or non-Hispanic Whites with children 18 years
or younger to compensate for the increased sample in
these demographic categories regardless of mover status.
Hispanic households receive a SCHIP adjustment factor of
8/18 and non-Hispanic non-White households and nonHispanic White households with children 18 years or
younger receive a SCHIP adjustment factor of 8/15. Table
11-3 summarizes these weight adjustments.
Households from the March eligible sample. The March
eligible sample households start with their basic CPS
weight, modified by their CPS special weighting factor, the
March CPS noninterview adjustment, the March CPS
first-stage ratio adjustment (as described in Chapter 10),
and the SCHIP adjustment factor.
SCHIP adjustment factor for the March eligible sample.
The SCHIP adjustment factor is applied to the March
eligible nonmover households that contain residents who
are Hispanic, non-Hispanic non-White, and non-Hispanic
Whites with children 18 years or younger to compensate
for the increased sample in these demographic categories.
Hispanic households receive a SCHIP adjustment factor of
8/18 and non-Hispanic non-White households and nonHispanic White resident households with children 18 years
or younger receive a SCHIP adjustment factor of 8/15.
Mover households and all other households receive a
SCHIP adjustment of 1. Table 11-3 summarizes these
weight adjustments.
Combined household eligible sample from November,
March, and April CPS. At this point, the eligible sample
from November, March, and April are combined. The
remaining adjustments are applied to this combined sample
file.
ADS second-stage ratio adjustment. The second-stage
ratio adjustment adjusts the ADS estimates so that they
agree with independent age, sex, race, and ethnicity
population controls as described in Chapter 10. The same
procedure, used for CPS, is used for the ADS.
Additional ADS weighting. After the ADS weight (the
weight through the second-stage procedure) is determined, the next step is to determine the final ADS weight.
There are two more weighting adjustments applied to the
ADS sample cases. One is for family equalization. Without
this adjustment there would be more married men than
married women. Weights, mostly of males, are adjusted to
11–8
Table 11-3. Summary of SCHIP Adjustment Factor
CPS month/Hispanic status
1
November
Hispanic
Hispanic
Month in sample
Month in sample
3
4
5
6
7
8
1
2
3
8/15
3
1
April
Non-Hispanic
8/18
3
8/15
02
4
5
6
7
8
8/18
02
3
1
Non-Hispanic
Hispanic
Nonmover
1
Non-Hispanic
March
2
Mover
0
2
8/15
1
8/18
1
8/15
8/18
02
8/15
8/18
02
8/15
8/18
8/15
02
8/15
1
Hispanics may be of any race.
Zero weight indicates the cases are ineligible for the ADS.
3
The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children ≤ 18 years old.
2
give a husband and wife the same weight, while maintaining the overall age/race/sex/Hispanic control totals. The
second adjustment is for members of the Armed Forces.
Family equalization. The family equalization procedure
categorizes adults (at least 15 years old) into seven groups
based on sex and household composition:
1. Female partners in female/female unmarried partner
households
2. All other civilian females
3. Married males, spouse present
4. Male partners in male/female unmarried partner households
5. Other civilian male heads
6. Male partners in male/male unmarried partner households
7. All other civilian males
Three different methods, dependent on the household
composition, are used to assign the ADS weight to other
members of the household. The methods are 1) assigning
the weight of the householder to the spouse or partner, 2)
averaging the weights of the householder and partner, or 3)
computing a ratio adjustment factor and multiplying the
factor by the ADS weight.
Armed forces. Male and female members of the Armed
Forces living off post or living with their families on post are
included in the ADS as long as there is at least one civilian
adult living in the same household, whereas the CPS
excludes all Armed Forces members. Households with no
civilian adults in the household, i.e., households with all
Armed Forces members, are excluded from the ADS. The
weights assigned to the Armed Forces members, included
in the ADS, is the same weight civilians receive through the
SCHIP adjustment. Control totals, used in the secondstage factor, do not include Armed Forces members so the
Armed Force members do not go through the secondstage ratio adjustment. During family equalization, a male
Armed Forces member with a spouse or partner is reassigned the weight of his spouse/partner.
SUMMARY
Although this discussion focuses on only two CPS
supplements, the HVS and the ADS, every supplement has
its own unique objectives. The additional questions, edits,
and imputations are tailored to each supplement’s data
needs. For many supplements this also means altering the
weighting procedure to reflect a different universe, account
for a modified sample, or adjust for a higher rate of
nonresponse. The weighting revisions discussed here for
HVS and ADS are only indicative of the types of modifications that might be used for a supplement.
12–1
Chapter 12.
Data Products From the Current Population Survey
INTRODUCTION
Information collected in the Current Population Survey
(CPS) is made available by both the Bureau of Labor
Statistics and the Census Bureau through broad publication programs which include news releases, periodicals,
and reports. CPS-based information is also available on
magnetic tapes, CD-ROM, and computer diskettes and can
be obtained online through the Internet. This chapter lists
many of the different types of products currently available
from the survey, describes the forms in which they are
available, and indicates how they can be obtained. This
chapter is not intended to be an exhaustive reference for all
information available from the CPS. Furthermore, given the
rapid ongoing improvements occurring in computer technology, it can be expected that greater numbers of the
CPS-based products will be electronically accessible in the
future.
BUREAU OF LABOR STATISTICS
Each month, employment and unemployment data are
published initially in The Employment Situation news release
about 2 weeks after data collection is completed. The
release includes a narrative summary and analysis of the
major employment and unemployment developments together
with tables containing statistics for the principal data series.
The news release is also available electronically on the
Internetandcanbeaccessedathttp://stats.bls.gov:80/newsrels.htm.
Subsequently, more detailed statistics are published in
Employment and Earnings, a monthly periodical. The detailed
tables provide information on the labor force, employment,
and unemployment by a number of characteristics, such as
age, sex, race, marital status, industry, and occupation.
Estimates of the labor force status and detailed characteristics of selected population groups not published on a
monthly basis, such as Vietnam-era veterans and Hispanics1 are published every quarter. Data are also published
quarterly on usual median weekly earnings classified by a
variety of characteristics. In addition, the January issue of
Employment and Earnings provides annual averages on
employment and earnings by detailed occupational categories, union affiliation, and employee absences.
About 25,000 of the monthly labor force data series plus
quarterly and annual averages are maintained in LABSTAT,
the BLS public database, on the Internet. They can be
1
Hispanics may be of any race.
accessed from http://stats.bls.gov/datahome.htm. In most
cases, these data are available from the inception of the
series through the current month. Approximately 250 of the
most important estimates from the CPS are presented
monthly and quarterly on a seasonally adjusted basis. The
CPS is used also for a program of special inquiries to
obtain detailed information from particular segments or for
particular characteristics of the population and labor force.
About four such special surveys are made each year. The
inquiries are repeated annually in the same month for
some topics, including the earnings and total incomes of
individuals and families (published by the Census Bureau);
the extent of work experience of the population during the
calendar year; the marital and family characteristics of
workers; the employment of school-age youth, high school
graduates and dropouts, and recent college graduates;
and the educational attainment of workers. Surveys are
also made periodically on subjects such as contingent
workers, job tenure, displaced workers, and disabled veterans.
Generally, the persons who provide information for the
monthly CPS questions also answer the supplemental
questions. Occasionally, the kind of information sought in
the special surveys requires the respondent to be the
person about whom the questions are asked. The results of
these special surveys are first published as news releases
and subsequently in the Monthly Labor Review or BLS
reports.
In addition to the regularly tabulated statistics described
above, special data can be generated through the use of
the CPS individual (micro) record files. These files contain
records of the responses to the survey questionnaire for all
individuals in the survey. While the microdata can be used
simply to create additional cross-sectional detail, an important feature of their use is the ability to match the records of
specific individuals at different points in time during their
participation in the survey. (The actual identities of these
individuals are protected on all versions of the files made
available to noncensus staff.) By matching these records,
data files can be created which lend themselves to some
limited longitudinal analysis and the investigation of shortrun labor market dynamics. An example is the statistics on
gross labor force flows, which indicate how many persons
move among the labor force status categories each month.
Microdata files are available for all months since January
1976 and for various months in prior years. These data are
made available on magnetic tape, CD-ROM, or diskette.
12– 2
Annual averages from the CPS for the four census
regions and nine divisions, the 50 states and the District of
Columbia, 50 large metropolitan areas, and 17 central
cities are published annually in Geographic Profile of
Employment and Unemployment. Data are provided on the
employed and unemployed by selected demographic and
economic characteristics.
Table 12– 1 provides a summary of the CPS data
products available from BLS.
U.S. CENSUS BUREAU
The U.S. Census Bureau has been analyzing data from
the Current Population Survey and reporting the results to
the public for over five decades. The reports provide
information on a recurring basis about a wide variety of
social, demographic, and economic topics. In addition,
special reports on many subjects have also been produced. Most of these reports have appeared in 1 of 3 series
issued by the Census Bureau: P-20, Population Characteristics; P-23, Special Studies; and P-60, Consumer Income.
Many of the reports are based on data collected as part of
the March demographic supplement to the CPS. However,
other reports use data from supplements collected in other
months (as noted in the listing below). A full inventory of
these reports as well as other related products is documented in: Subject Index to Current Population Reports
and Other Population Report Series, CPR P23-192, which
is available from the Government Printing Office, or the
Census Bureau. Most reports have been issued in paper
form; more recently, some have been made available on
the Internet (http://www.census.gov). Generally, reports
are announced by press release, and are released to the
public via the Census Bureau Public Information Office.
Census Bureau Report Series
P-20, Population Characteristics. Regularly recurring reports
in this series include topics such as geographic mobility,
educational attainment, school enrollment (October supplement), marital status, households and families, Hispanic
origin, the Black population, fertility (June supplement),
voter registration and participation (November supplement),
and the foreign-born population.
P-23, Special Studies. Information pertaining to special
topics, including one-time data collections, as well as
research on methods and concepts are produced in this
series. Examples of topics include computer ownership
and usage, child support and alimony, ancestry, language,
and marriage and divorce trends.
P-60, Consumer Income. Regularly recurring reports in
this series include information concerning families, individuals, and households at various income and poverty levels,
shown by a variety of demographic characteristics. Other
reports focus on health insurance coverage and other
noncash benefits.
In addition to the population data routinely reported from
the CPS, Housing Vacancy Survey (HVS) data are collected from a sample of vacant housing units in the Current
Population Survey (CPS) sample. Using these data, quarterly and annual statistics are produced on rental vacancy
rates and home ownership rates for the United States, the
four census regions, location inside and outside metropolitan areas (MAs), the 50 states and the District of Columbia,
and the 75 largest MAs. Information is also made available
on national home ownership rates by age of householder,
family type, race, and Hispanic origin. A press release is
issued each quarter as well as quarterly and annual data
tables on the Internet.
Supplement Data Files
Public use microdata files containing supplement data
are available from the Census Bureau. These files contain
the full battery of basic labor force and demographic data
along with the supplement data. A standard documentation
package containing a record layout, source and accuracy
statement, and other relevant information is included with
each file. (The actual identities of the individuals surveyed
are protected on all versions of the files made available to
noncensus staff.) These files can be purchased through the
Customer Services Branch of the Census Bureau and are
available in either tape or CD-ROM format. The CPS
homepage is the other source for obtaining these files.
Eventually, we plan to add most historical files to the site
along with all current and future files.
12– 3
Table 12– 1. Bureau of Labor Statistics Data Products From the Current Population Survey
Product
Description
Periodicity
Source
Cost
News Releases
College Enrollment and Work
Activity of High School
Graduates
An analysis of the college enrollment and work activity of the prior
year’s high school graduates by a variety of characteristics
Annual
October CPS
supplement
Free (1)
Contingent and Alternative
Employment Arrangements
An analysis of workers with ‘‘contingent’’ employment arrangements (lasting less than 1 year) and alternative arrangements
including temporary and contract employment by a variety of
characteristics
Biennial
January CPS
supplement
Free (1)
Displaced Workers
An analysis of workers who lost jobs in the prior 3 years due to
plant or business closings, position abolishment, or other reasons
by a variety of characteristics
Biennial
February CPS
supplement
Free (1)
Employment Situation of
Vietnam-Era Veterans
An analysis of the work activity and disability status of persons who
served in the Armed Forces during the Vietnam era
Biennial September CPS
supplement
Free (1)
Job Tenure of American
Workers
An analysis of employee tenure by industry and a variety of
demographic characteristics
Biennial
February CPS
supplement
Free (1)
State and Regional
Unemployment
An analysis of state and regional employment and unemployment
Annual
CPS annual
averages
Free (1)
The Employment Situation
Seasonally adjusted and unadjusted data on the Nation’s employed
and unemployed workers by a variety of characteristics
Monthly (2)
Monthly CPS
Free (1)
Union Membership
An analysis of the union affiliation and earnings of the Nation’s
employed workers by a variety of characteristics
Annual
Monthly CPS;
outgoing rotation groups
Free (1)
Usual Weekly Earnings of Wage Median usual weekly earnings of full- and part-time wage and
and Salary Workers
salary workers by a variety of characteristics
Quarterly (3)
Monthly CPS;
outgoing rotation groups
Free (1)
Annual
March CPS
supplement
Free (1)
Work Experience of the
Population
An examination of the employment and unemployment experience
of the population during the entire preceding calendar year by a
variety of characteristics
Periodicals
Employment and Earnings
A monthly periodical providing data on employment, unemployment, hours, and earnings for the Nation, states, and metropolitan
areas
Monthly (3)
CPS; other
surveys and
programs
$35.00 domestic; $43.75
foreign
Monthly Labor Review
A monthly periodical containing analytical articles on employment,
unemployment, and other economic indicators, book reviews, and
numerous tables of current labor statistics
Monthly
CPS; other
surveys and
programs
$29.00 domestic; $36.25 foreign
Other Publications
A Profile of the Working Poor
An annual report on workers whose families are in poverty by work
experience and various characteristics
Annual
March CPS
supplement
Free
Geographic Profile of Employment and Unemployment
An annual publication of employment and unemployment data for
regions, states, and metropolitan areas by a variety of characteristics
Annual
CPS annual
averages
$9.00
Issues in Labor Statistics
Brief analysis of important and timely labor market issues
Occasional
CPS; other
surveys and
programs
Free
12– 4
Table 12– 1. Bureau of Labor Statistics Data Products From the Current Population Survey—Con.
Product
Description
Periodicity
Source
Cost
Microdata Files
Job Tenure and Occupational
Mobility
Biennial
February CPS
supplement
(4)
Displaced Workers
Biennial
February CPS
supplement
(4)
Contingent Work
Biennial
January CPS
supplement
(4)
Annual
March CPS
supplement
(4)
Biennial September CPS
supplement
(4)
Annual Demographic Survey
Veterans
Annual
October CPS
supplement
(4)
Occasional
May CPS
supplement
( 4)
Annual
Annual CPS
(4)
National Labor Force Data
Monthly
Labstat(5)
(1 )
Regional Data
Monthly
Labstat(5)
(1 )
National Labor Force Data
Monthly
Microfiche
Free
Regional, State, and Area Data
Monthly
Microfiche
Free
School Enrollment
Work Schedules/Home-Based
Work
Annual Earnings (outgoing
rotation groups)
Time Series (Macro) Files
Unpublished Tabulations
1
2
3
4
5
Accessible from the Internet (http://stats.bls.gov:80/newsrels.htm).
About 3 weeks following period of reference.
About 5 weeks after period of reference.
Diskettes ($80); cartridges ($165-$195); tapes ($215-$265); and CD-ROMs ($150).
Electronic access via the Internet (http://stats.bls.gov).
Note: Prices noted above are subject to change.
13–1
Chapter 13.
Overview of Data Quality Concepts
INTRODUCTION
It is far easier to put out a figure than to accompany it
with a wise and reasoned account of its liability to
systematic and fluctuating errors. Yet if the figure is …
to serve as the basis of an important decision, the
accompanying account may be more important than
the figure itself. John W. Tukey (1949, p. 9)
The quality of any estimate based on sample survey
data can and should be examined from two perspectives.
The first is based on the mathematics of statistical science,
and the second stems from the fact that survey measurement is a production process conducted by human beings.
From both perspectives, survey estimates are subject to
error, and to avoid misusing or reading too much into the
data, we should use them only after their potential error of
both sorts has been examined relative to the particular use
at hand.
In this chapter, we give an overview of these two
perspectives on data quality, discuss their relationship to
each other from a conceptual viewpoint, and define a
number of technical terms. The definitions and discussion
are applicable to all sample surveys, not just the Current
Population Survey (CPS). Succeeding chapters go into
greater detail about the specifics as they relate to CPS.
QUALITY MEASURES IN STATISTICAL SCIENCE
The statistical theory of finite population sampling is
based on the concept of repeated sampling under fixed
conditions. First, a particular method of selecting a sample
and aggregating the data from the sample units into an
estimate of the population parameter is specified. The
method for sample selection is referred to as the sample
design (or just the design). The procedure for producing the
estimate is characterized by a mathematical function known
as an estimator. After the design and estimator have been
determined, a sample is selected and an estimate of the
parameter is computed. The difference between the value
of the estimate and the population parameter is referred to
here as the sampling error, and it will vary from sample to
sample (Särndal, Swensson, and Wretman, 1992, p. 16).
Properties of the sample design-estimator methodology
are determined by looking at the distribution of estimates
that results from taking all possible samples that could be
selected using the specified methodology. The expected
(or mean) value of the squared sampling errors over all
possible samples is known as the mean squared error. The
mean squared error is generally accepted as the standard
overall measure of the quality of a proposed designestimator methodology.
The mean value of the individual estimates is referred as
the expected value of the estimator. The difference between
the expected value of a particular estimator and the value
of the population parameter is known as sampling bias.
When the bias of the estimator is zero, the estimator is said
to be unbiased. The mean value of the squared difference
of the values of the individual estimates and the expected
value of the estimator is known as the sampling variance of
the estimator. The variance measures the magnitude of the
variation of the individual estimates about their expected
value while the mean squared error measures the magnitude of the variation of the estimates about the value of the
population parameter of interest. It can be shown that the
mean squared error can be decomposed into the sum of
the variance and the square of the bias. Thus, for an
unbiased estimator, the variance and the mean squared
error are equal.
Quality measures of a design-estimator methodology
expressed in this way, that is, based on mathematical
expectation assuming repeated sampling, are inherently
grounded on the assumption that the process is correct
and constant across sample repetitions. Unless the measurement process is uniform across sample repetitions, the
mean squared error is not by itself a full measure of the
quality of the survey results.
The assumptions associated with being able to compute
any mathematical expectation are extremely rigorous and
rarely practical in the context of most surveys. For example,
the basic formulation for computing the true mean squared
error requires that there be a perfect list of all units in the
universe population of interest, that all units selected for a
sample provide all the requested data, that every interviewer be a clone of an ideal interviewer who follows a
predefined script exactly and interacts with all varieties of
respondents in precisely the same way, and that all respondents comprehend the questions in the same way and
have the same ability to recall from memory the specifics
needed to answer the questions.
Recognizing the practical limitations of these assumptions, sampling theorists continue to explore the implications of alternative assumptions which can be expressed in
terms of mathematical models. Thus, the mathematical
expression for variance has been decomposed in various
13– 2
ways to yield expressions for statistical properties that
include not only sampling variance, but simple response
variance (a measure of the variability among the possible
responses of a particular respondent over repeated administrations of the same question) (Hansen, Hurwitz, and
Bershad, 1961) and correlated response variance, one
form of which is interviewer variance (a measure of the
variability among responses obtained by different interviewers over repeated administrations). Similarly, when a particular design-estimator fails over repeated sampling to
include a particular set of population units in the sampling
frame or to ensure that all units provide the required data,
bias can be viewed as having components such as coverage bias, unit nonresponse bias, or item nonresponse bias
(Groves, 1989). For example, a survey administered solely
by telephone could result in coverage bias for estimates
relating to the total population if the nontelephone households were different than the telephone households with
respect to the characteristic being measured (which almost
always occurs).
One common theme of these types of models is the
decomposition of total mean squared error into one set of
components resulting from the fact that estimates are
based on a sample of units rather than the entire population (sampling error) and another set of components due to
alternative specifications of procedures for conducting the
sample survey (nonsampling error). (Since nonsampling
error is defined negatively, it ends up being a catch-all term
for all errors other than sampling error.) Conceptually,
nonsampling error in the context of statistical science has
both variance and bias components. However, when total
mean squared error is decomposed mathematically to
include a sampling error term and one or more other
‘‘nonsampling error’’ terms, it is often difficult to categorize
such terms as either variance or bias. The term nonsampling error is used rather loosely in the survey literature to
denote mean squared error, variance, or bias in the precise
mathematical sense and to imply error in the more general
sense of process mistakes (see next section).
Some nonsampling error components which are conceptually known to exist have yet to be expressed in practical
mathematical models. Two examples are the bias associated with the use of a particular set of interviewers and the
variance associated with the selection of one of the numerous possible sets of questions. In addition, the estimation
of many nonsampling errors — and sampling bias — is
extremely expensive and difficult or even impossible in
practice. The estimation of bias, for example, requires
knowledge of the truth, which may be sometimes verifiable
from records (e.g., number of hours paid for by employer)
but often is not verifiable (e.g., number of hours actually
worked). As a consequence, survey organizations typically
concentrate on estimating the one component of total
mean squared error for which practical methods have been
developed — variance.
Since a survey is generally conducted only once with
one specific sample of units, it is impossible to compute the
actual sampling variance. In simple cases, it is frequently
possible to construct an unbiased estimator of variance. In
the case of complex surveys like CPS, estimators have
been developed which typically rely on the proposition —
usually well-grounded — that the variability among estimates based on various subsamples of the one actual
sample is a good proxy for the variability among all the
possible samples like the one at hand. In the case of CPS,
160 subsamples or replicates are used in variance estimation for the 1990 design. (For more specifics, see Chapter
14.) It is important to note that the estimates of variance
resulting from the use of this and similar methods are not
merely estimates of sampling variance. The variance estimates include the effects of some nonsampling errors,
such as response variance and intra-interviewer correlation. On the other hand, users should be aware of the fact
that for some statistics these estimates of standard error
might be significant underestimates of total error, an important consideration when making inferences based on survey data.
To draw conclusions from survey data, samplers rely on
the theory of finite population sampling from a repeated
sampling perspective: If the specified sample design-estimator
methodology were implemented repeatedly and the sample
size sufficiently large, the probability distribution of the
estimates would be very close to a normal distribution.
Thus, one could safely expect 90 percent of the estimates to be within two standard errors of the mean of all
possible sample estimates (standard error is the square
root of the estimate of variance) (Gonzalez et al., 1975;
Moore, 1997). However, one cannot claim that the probability is .90 that the true population value falls in a
particular interval. In the case of a biased estimator due to
nonresponse, undercoverage, or other types of nonsampling error, confidence intervals may not cover the population parameter at the desired 90 percent rate. In such
cases, a standard error estimator may indirectly account
for some elements of nonsampling error in addition to
sampling error and lead to confidence intervals having
greater than the nominal 90 percent coverage. On the
other hand, if the bias is substantial, confidence intervals
can have less than the desired coverage.
QUALITY MEASURES IN STATISTICAL
PROCESS MONITORING
The process of conducting a survey includes numerous
steps or components, such as defining concepts, translating concepts into questions, selecting a sample of units
from what may be an imperfect list of population units,
hiring and training interviewers to ask persons in the
sample unit the questions, coding responses to questions
into predefined categories, and creating estimates which
take into account the fact that not everyone in the population of interest had a chance to be in the sample and not all
of those in the sample elected to provide responses. It is a
13– 3
process where the possibility exists at each step of making
a mistake in process specification and in deviating during
implementation from the predefined specifications.
For example, we now recognize that the initial labor
force question used in CPS for many years (‘‘What were
you doing most of last week--...’’) was problematic to many
respondents (see Chapter 6). Moreover, many interviewers
tailored their presentation of the question to particular
respondents, for example, saying ‘‘What were you doing
most of last week—working, going to school, etc.?’’ if the
respondent was of school age. Having a problematic
question is a mistake in process specification; varying
question wording in a way not prespecified is a mistake in
process implementation.
Errors or mistakes in process contribute to nonsampling
error in that they would contaminate results even if the
whole population were surveyed. Parts of the overall
survey process which are known to be prone to deviations
from the prescribed process specifications and thus could
be potential sources of nonsampling error in the CPS are
discussed in Chapter 15, along with the procedures put in
place to limit their occurrence.
A variety of quality measures have been developed to
describe what happens during the survey process. These
measures are vital for managers and staff working on a
survey as reflecting process quality, but they can also be
useful to users of the various products of the survey
process — both individual responses and their aggregations into statistics, since they can aid in determining a
particular product’s potential limitations and whether it is
appropriate for the task at hand. Chapter 16 contains a
discussion of quality indicators and, in a few cases, their
potential relationship to nonsampling errors.
SUMMARY
The quality of estimates made from any survey, including CPS, is a function of innumerable decisions made by
designers and implementers. As a general rule of thumb,
designers make decisions aimed at minimizing mean squared
error within given cost constraints. Practically speaking,
statisticians are often compelled to make decisions on
sample designs and estimators based on variance alone;
however, in the case of CPS, the availability of external
population estimates and data on rotation group bias
makes it possible to do more than that. Designers of
questions and data collection procedures tend to focus on
limiting bias and assume the specification of exact question
wording and ordering will naturally limit the introduction of
variance. Whatever the theoretical focus of the designers,
the accomplishment of the goal is heavily dependent upon
those responsible for implementing the design.
Implementers of specified survey procedures, like interviewers and respondents, are presumably concentrating
on doing the best job they know how to do given time and
knowledge constraints. Consequently, process monitoring
through quality indicators, such as coverage and response
rates, is necessary to determine when additional training or
revisions in process specification are needed. Continuing
process improvement must be a vital component of survey
management if the quality goals set by designers are to be
achieved.
REFERENCES
Gonzalez, M. E., Ogus, J. L., Shapiro, G., and Tepping,
B. J. (1975), ‘‘Standards for Discussion and Presentation of
Errors in Survey and Census Data,’’ Journal of the
American Statistical Association, 70, No. 351, Part II,
5-23.
Groves, R. M. (1989), Survey Errors and Survey
Costs, New York: John Wiley & Sons.
Hansen, M. H., Hurwitz, W. N., and Bershad, M. A.
(1961), ‘‘Measurement Errors in Censuses and Surveys,’’
Bulletin of the International Statistical Institute, 38(2),
pp. 359-374.
Moore, D. S. (1997), Statistics Concepts and Controversies, 4th Edition, New York: W. H. Freeman.
Särndal, C., Swensson, B., and Wretman, J. (1992),
Model Assisted Survey Sampling, New York: SpringerVerlag.
Tukey, J. W. (1949), ‘‘Memorandum on Statistics in the
Federal Government,’’ American Statistician, 3, No. 1,
pp. 6-17; No. 2, pp. 12-16.
14–1
Chapter 14.
Estimation of Variance
INTRODUCTION
The following two objectives are considered in estimating variances of the major statistics of interest for the
Current Population Survey (CPS):
1. Estimate the variance of the survey estimates for use
in various statistical analyses.
2. Evaluate the effect of each of the stages of sampling
and estimation on the overall precision of the survey
estimates for the evaluation of the survey design.
CPS variance estimates take into account the magnitude of the sampling error as well as the effects of some
nonsampling errors, such as response variance and intrainterviewer correlation. Certain aspects of the CPS sample
design, such as the use of one sample PSU per nonselfrepresenting stratum and the use of systematic sampling
within PSUs, make it impossible to obtain a completely
unbiased estimate of the total variance. The use of ratio adjustments in the estimation procedure also contributes to
this problem. Although imperfect, the current variance estimation procedure is accurate enough for all practical uses
of the data, as well as reflecting the effects of the separate
stages of sample selection and estimation on the total
variance. Variance estimates of selected characteristics
and tables, which also show the effects of estimation steps
on variances, are presented at the end of this chapter.
VARIANCE ESTIMATES BY THE REPLICATION
METHOD
Replication methods are able to provide satisfactory
estimates of variance for a wide variety of designs using
probability sampling, even when complex estimation procedures are used. This method requires that the sample
selection, the collection of data, and the estimation procedures be independently carried through (replicated) several times. The dispersion of the resulting estimates can be
used to measure the variance of the full sample.
The Method
Obviously, one would not consider repeating the entire
Current Population Survey several times each month simply to obtain variance estimates. A practical alternative is to
draw a set of random subsamples from the full sample
surveyed each month, using the same principles of selection as are used for the full sample, and to apply the regular
CPS estimation procedures to these subsamples. We refer
to these subsamples as replicates. The number of replicates to use is based on a trade-off between cost and the
reliability of the variance estimator; that is, increasing the
number of replicates decreases the variance of the variance estimator.
Prior to the design introduced after the 1970 census,
variance estimates were computed using 40 replicates.
The replicates were subjected to only the second-stage
ratio adjustment for the same age-sex-race categories
used for the full sample at the time. The noninterview and
first-stage ratio adjustments were not replicated. Even with
these simplifications, limited computer capacity allowed for
the computation of variances for only 14 characteristics.
For the 1970 design, an adaptation of the Keyfitz method of
calculating variances was used. These variance estimates
were derived using the Taylor approximation, dropping
terms with derivatives higher than the first. By 1980,
improvements in computer memory capacity allowed for
the calculation of variance estimates for many characteristics with replication of all stages of the weighting through
compositing. Note that the seasonal adjustment has not
been replicated. A study from an earlier design indicated
that the seasonal adjustment of CPS estimates had relatively little impact on the variances; however, it is not known
what impact this adjustment would have on the current
design variances.
Starting with the 1980 design, variances were computed
using a modified balanced half-sample approach. The
sample was divided to form 48 replicates that retained all
the features of the sample design, for example, the stratification and the within-PSU sample selection. For total
variance, a pseudo-first-stage design was imposed on the
CPS by dividing large self-representing (SR) PSUs into
smaller areas called Standard Error Computation Units
(SECUs) and combining small nonself-representing (NSR)
PSUs into paired strata or pseudostrata. One NSR PSU
was selected randomly from each pseudostratum for each
replicate. Forming these pseudostrata was necessary since
the first stage of the sample design has only one NSR PSU
per stratum in sample. However, pairing the original strata
for variance estimation purposes creates an upward bias in
the variance estimator. For self-representing PSUs each
SECU was divided into two panels, and one panel was
selected for each replicate. One column of a 48 by 48
Hadamard orthogonal matrix was assigned to each SECU
or pseudostratum. The unbiased weights were multiplied
by replicate factors of 1.5 for the selected panel and 0.5 for
14–2
the other panel in the SR SECU or NSR pseudostratum
(Fay, Dippo, and Morganstein, 1984). Thus the full sample
was included in each replicate, but differing weights for the
half-samples were determined by the matrix. These 48
replicates were processed through all stages of the CPS
weighting through compositing. The estimated variance for
the characteristic of interest was computed by summing a
squared difference between each replicate estimate (Ŷr)
and the full sample estimate (Ŷ0) The complete formula1 is
Var(Ŷ0) =
4
48
48
兺(Ŷr - Ŷ0)2.
r=1
Due to costs and computer limitations, variance estimates
were calculated for only 13 months (January 1987 through
January 1988) and for about 600 estimates at the national
level. Replication estimates of variances at the subnational
level were not reliable because of the small number of
SECUs available (Lent, 1991). Based on the 13 months of
variance estimates, generalized sampling errors (explained
later) were calculated. (See Wolter 1985 or Fay 1984, 1989
for more details on half-sample replication for variance
estimation.)
METHOD FOR ESTIMATING VARIANCE FOR
1990 DESIGN
The general goal of the current variance estimation
methodology, the method in use since July 1995, is to
produce consistent variances and covariances for each
month over the entire life of the design. Periodic maintenance reductions in the sample size and the continuous
addition of new construction to the sample complicated the
strategy needed to achieve this goal. However, research
has shown that variance estimates are not adversely
affected as long as the cumulative effect of the reductions
is less than 20 percent of the original sample size (Kostanich, 1996). Assigning all future new construction sample to
replicates when the variance subsamples are originally
defined provides the basis for consistency over time in the
variance estimates.
The current approach to estimating the 1990 design
variances is called successive difference replication. The
theoretical basis for the successive difference method was
discussed by Wolter (1984) and extended by Fay and Train
(1995) to produce the successive difference replication
method used for the CPS. The following is a description of
the application of this method. Successive USUs2 (ultimate
sampling units) formed from adjacent hit strings (see
Chapter 3) are paired in the order of their selection to take
advantage of the systematic nature of the CPS within-PSU
sampling scheme. Each USU usually occurs in two consecutive pairs; for example, (USU1, USU2), (USU2, USU3),
(USU3, USU4), etc. A pair then is similar to a SECU in the
1980 design variance methodology. For each USU within a
PSU, two pairs (or SECUs) of neighboring USUs are
defined based on the order of selection — one with the
USU selected before and one with the USU selected after
it. This procedure allows USUs adjacent in the sort order to
be assigned to the same SECU, thus better reflecting the
systematic sampling in our variance estimator. Also, the
large increase in the number of SECUs and in the number
of replicates (160 vs. 48) over the 1980 design increases
the precision of the variance estimator.
Replicate Factors for Total Variance
Total variance is composed of two types of variance, the
variance due to sampling of housing units within PSUs
(within-PSU variance) and the variance due to the selection of a subset of all NSR PSUs (between-PSU variance).
Replicate factors are calculated using a 160 by 1603
Hadamard orthogonal matrix. To produce estimates of total
variance, replicates are formed differently for SR and NSR
sample. Note that between-PSU variance cannot be estimated directly using this methodology. Rather, it is the
difference between the estimates of total variance and
within-PSU variance. NSR strata are combined into pseudostrata within each state, and one NSR PSU from the
pseudostratum is randomly assigned to each panel of the
replicate as in the 1980 design variance methodology.
Replicate factors of 1.5 or 0.5 adjust the weights for the
NSR panels. These factors are assigned based on a single
row from the Hadamard matrix and are further adjusted to
account for the unequal sizes of the original strata within
the pseudostratum (Wolter, 1985). In most cases these
pseudostrata consist of a pair of strata except where an
odd number of strata within a state requires that a triplet be
formed. In this case, two rows from the Hadamard matrix
are assigned to the pseudostratum resulting in replicate
factors of about 0.5, 1.7, and 0.8; or 1.5, 0.3, and 1.2 for the
three PSUs. All USUs in a pseudostratum are assigned the
same row number(s).
For SR sample, two rows of the Hadamard matrix are
assigned to each pair of USUs creating replicate factors,
fr for r = 1,...,160
3
fir ⫽ 1 ⫹ 共2兲
1
Usually balanced half-sample replication uses replicate factors of 2
and 0 with the formula,
1 k
Var(Ŷo) = 兺(Ŷr - Ŷo)2.
k r=1
where k is the number of replicates. The factor of 4 in our variance
estimator is the result of using replicate factors of 1.5 and 0.5.
2
An ultimate sampling unit is usually a group of four neighboring
housing units.
⫺
2
3
ai⫹1,r ⫺ 共2兲
⫺
2
ai⫹2,r
where ai,r equals a number in the Hadamard matrix (+1 or
-1) for the ith USU in the systematic sample. This formula
yields replicate factors of approximately 1.7, 1.0, or 0.3.
3
Rows 1 and 81 have been dropped from the matrix.
14–3
As in the 1980 methodology, the unbiased weights
(baseweight x special weighting factor) are multiplied by
the replicate factors to produce unbiased replicate weights.
These unbiased replicate weights are further adjusted
through the noninterview adjustment, the first-stage ratio
adjustment, the second-stage ratio adjustment, and compositing just as the full sample is weighted. A variance
estimator for the characteristic of interest is a sum of
squared differences between each replicate estimate (Ŷr)
and the full sample estimate (Ŷo) The formula is
Var(Ŷo) =
4
160
160
r=1
兺 (Ŷr - Ŷo)2.
Note that the replicate factors 1.7, 1.0, and 0.3 for the
self-representing portion of the sample were specifically
constructed to yield ‘‘4’’ in the above formula in order that
the formula remains consistent between SR and NSR
areas (Fay and Train, 1995).
Replicate Factors for Within-PSU Variance
The above variance estimator can also be used for
within-PSU variance. The same replicate factors used for
total variance are applied to SR sample. For NSR sample,
alternate row assignments are made for USUs to form
pairs of USUs in the same manner that was used for the
SR assignments. Thus for within-PSU variance all USUs
(both SR and NSR) have replicate factors of approximately
1.7, 1.0, or 0.3.
The successive difference replication method is used to
calculate total national variances and within-PSU variances for some states and metropolitan areas. Improved
reliability for within-PSU subnational variance estimates is
expected due to the increased number of SECUs over the
1980 design. The reliability of between-PSU variance
estimates for subnational variance estimates (e.g., state
estimates) and the reliability of national estimates of variance should be about the same as for the 1980 design. For
more detailed information regarding the formation of replicates, see the internal Census Bureau memorandum (Gunlicks, 1996).
VARIANCES FOR STATE AND LOCAL AREA
ESTIMATES
For estimates at the national level, total variances are
estimated from the sample data by the successive difference replication method previously described. For local
areas that are coextensive with one or more sample PSUs,
a variance estimator can be derived using the methods of
variance estimation used for the SR portion of the national
sample. It is anticipated that these estimates of variance
will be more reliable than the 1980 procedure for areas with
comparable sample sizes due to the change in methodology. However, estimates for states and areas which have
substantial contributions from NSR sample areas have
variance estimation problems that are much more difficult
to resolve.
Complicating Factors for State Variances
Most states contain a small number of NSR sample
PSUs. Pairing them into pseudostrata reduces the number
of NSR SECUs still further and increases reliability problems. Also, the component of variance resulting from
sampling PSUs can be more important for state estimates
than for national estimates in states where the proportion of
the population in NSR strata is larger than the national
average.
Further, creating pseudostrata for variance estimation
purposes introduces a between-stratum variance component that is not in the sample design, causing overestimation of the true variance. The between-PSU variance
which includes the between-stratum component is relatively small at the national level for most characteristics, but
it can be much larger at the state level (Gunlicks, 1993;
Corteville, 1996). Thus, this additional component should
be accounted for when estimating state variances.
Research is in progress to produce improved state and
local variances utilizing the within-PSU variances obtained
from successive difference replication and perhaps some
modeling techniques. These variances will be provided as
they become available.
GENERALIZING VARIANCES
With some exceptions, the standard errors provided with
published reports and public data files are based on
generalized variance functions (GVFs). The GVF is a
simple model that expresses the variance as a function of
the expected value of the survey estimate. The parameters
of the model are estimated using the direct replicate
variances discussed above. These models provide a relatively easy way to obtain an approximate standard error on
numerous characteristics.
Why Generalized Standard Errors Are Used
It would be possible to compute and show an estimate of
the standard error based on the survey data for each
estimate in a report, but there are a number of reasons why
this is not done. A presentation of the individual standard
errors would be of limited use, since one could not possibly
predict all of the combinations of results that may be of
interest to data users. Also, for estimates of differences and
ratios that users may compute, the published standard
errors would not account for the correlation between the
estimates.
Most importantly, variance estimates are based on sample
data and have variances of their own. The variance estimate for a survey estimate for a particular month generally
14–4
has less precision than the survey estimate itself. This
means that the estimates of variance for the same characteristic may vary considerably from month-to-month or for
related characteristics (that might actually have nearly the
same level of precision) in a given month. Therefore, some
method of stabilizing these estimates of variance, for
example, by generalization or by averaging over time, is
needed to improve their reliability.
Experience has shown that certain groups of CPS
estimates have a similar relationship between their variance and expected value. Modeling or generalization may
provide more stable variance estimates by taking advantage of these similarities.
The Generalization Method
The GVF that is used to estimate the variance of an
estimated population total, X, is of the form
Var共X̂) ⫽ aX2 ⫹ bX
共14.1兲
where a and b are two parameters estimated using least
squares regression. The rationale for this form of the GVF
model is the assumption that the variance of X̂ can be
expressed as the product of the variance from a simple
random sample for a binomial random variable and a
‘‘design effect.’’ The design effect (deff) accounts for the
effect of a complex sample design relative to a simple
random sample. Defining P = X/N as the proportion of the
population having the characteristic X, where N is the
population size, and Q = 1-P, the variance of the estimated
total X̂, based on a sample of n individuals from the
population, is
Var共X̂兲 ⫽
N2PQ共deff兲
共14.2兲
n
In generalizing variances, all estimates that follow a
common model such as 14.1 (usually the same characteristics for selected demographic or geographic subgroups)
are grouped together. This should give us estimates in the
same group that have similar design effects. These design
effects incorporate the effect of the estimation procedures,
particularly the second stage, as well as the effect of the
sample design. In practice, the characteristics should be
clustered similarly by PSU, by USU, and among persons
within housing units. For example, estimates of total persons classified by a characteristic of the housing unit or of
the household, such as the total urban population, number
of recent migrants, or persons of Hispanic4 origin, would
tend to have fairly large design effects. The reason for this
is that these characteristics usually appear among all
persons in the sample household and often among all
households in the USU as well. On the other hand, lower
design effects would result for estimates of labor force
status, education, marital status, or detailed age categories, since these characteristics tend to vary among members of the same household and among households within
a USU.
Notice also that for many subpopulations of interest, N is
a control total used in the second-stage ratio adjustment. In
these subpopulations, as X approaches N, the variance of
X approaches zero, since the second-stage ratio adjustment guarantees that these sample population estimates
match independent population controls5 (Chapter 10). The
GVF model satisfies this condition. This generalized variance model has been used since 1947 for the CPS and its
supplements, although alternatives have been suggested
and investigated from time to time (Valliant, 1987). The
model has been used to estimate standard errors of means
or totals. Variances of estimates based on continuous
variables (e.g., aggregate expenditures, amount of income,
etc.) would likely fit a different functional form better.
The parameters, a and b, are estimated by use of the
model for relative variance
This can be written as
Var(X̂) ⫽ ⫺ 共deff兲
( )( )
N X2
n
N
⫹ 共deff兲
()
N
n
Vx2 ⫽ a ⫹
X.
Letting
a⫽⫺
b
N
and
b⫽
共deff兲N
n
gives the functional form
Var共X̂兲 ⫽ aX2 ⫹ bX.
We choose
a⫽ ⫺
b
X
,
where the relative variance (Vx2) is the variance divided by
the square of the expected value of the estimate. The a and
b parameters are estimated by fitting a model to a group of
related estimates and their estimated relative variances.
The relative variances are calculated using the successive
difference replication method.
The model fitting technique is an iterative weighted least
squares procedure, where the weight is the inverse of the
square of the predicted relative variance. The use of these
weights prevents items with large relative variances from
unduly influencing the estimates of the a and b parameters.
b
N
where N is a control total so that the variance will be zero
when X = N.
4
Hispanics may be of any race.
The variance estimator assumes no variance on control totals, even
though they are estimates.
5
14–5
Usually at least a year’s worth of data are used in this
model fitting process and each group of items should
comprise at least 20 characteristics with their relative
variances, although occasionally fewer characteristics are
used.
Direct estimates of relative variances are required for
estimates covering a wide range, so that observations are
available to ensure a good fit of the model at high, low, and
intermediate levels of the estimates. It must be remembered that, by using a model to estimate the relative
variance of an estimate in this way, we introduce some
error, since the model may substantially and erroneously
modify some legitimately extreme values. Generalized
variances are computed for estimates of month-to-month
change as well as for estimates of monthly levels. Periodically, the a and b parameters are updated to reflect
changes in the levels of the population totals or changes in
the ratio (N/n) which result from sample reductions. This
can be done without recomputing direct estimates of
variances as long as the sample design and estimation
procedures are essentially unchanged (Kostanich, 1996).
How the Relative Variance Function is Used
After the parameters a and b of expression (14.1) are
determined, it is a simple matter to construct a table of
standard errors of estimates for publication with a report. In
practice, such tables show the standard errors that are
appropriate for specific estimates, and the user is instructed
to interpolate for figures not explicitly shown in the table.
However, many reports present a list of the parameters,
enabling data users to compute generalized variance estimates directly. A good example is a recent monthly issue of
Employment and Earnings (U.S. Department of Labor)
from which the following table was taken.
Example:
The approximate standard error, sXˆ , of an estimated
monthly level X̂ can be obtained with a and b from the
above table and the formula
sXˆ ⫽
公aX̂2 ⫹ bX̂
Assume that in a given month there are an estimated
6 million unemployed men in the civilian labor force
(X̂ = 6,000,000). From Table 14–1
a = -0.000015749 and b = 2464.91 , so
sx ⫽
公共⫺0.000015749兲共6,000,000兲2 ⫹ 共2464.91兲共6,000,000兲 ⬇ 119,000
An approximate 90-percent confidence interval for the
monthly estimate of unemployed men is between 5,810,000
and 6,190,000 [or 6,000,000 ± 1.6(119,000)].
VARIANCE ESTIMATES TO DETERMINE
OPTIMUM SURVEY DESIGN
The sample design and the estimation procedures used
in the CPS have changed many times since the start of the
Table 14–1. Parameters for Computation of Standard
Errors for Estimates of Monthly Levels1
Characteristic
Unemployment:
Total or White. . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . .
a
b
−0.000015749
0.000191460
−0.000098631
2464.91
2621.89
2704.53
1
Parameters reflect variances for 1995. See Appendix H for variance
estimates since January 1996.
2
Hispanics may be of any race.
survey. These changes result from a continuing program of
research and development with the objective of optimizing
the use of the techniques and resources which are available at a given time. Changes also occur when reliability
requirements are revised. Estimates of the components of
variance attributable to each of the several stages of
sampling and the effect of the separate steps in the
estimation procedure on the variance of the estimate are
needed to develop an efficient sample design.
The tables which follow show variance estimates computed, using replication methods, by type (total and withinPSU), and by stage of estimation. Averages over 6 months
have been used to improve the reliability of the estimated
monthly variances. The 6-month period, July-December
1995, was used for estimation because the sample design
was unchanged throughout the period. Appendix H provides similar information on variances and components of
variance after the January 1996 sample reduction.
Variance Components Due to Stages of
Sampling
Table 14–2 indicates, for the first- and second-stage
combined (FSC) estimate, how the several stages of
sampling affect the total variance of each of the given
characteristics. The FSC estimate is the estimate after the
first- and second-stage ratio adjustments are applied.
Within-PSU variance and total variance are computed as
described earlier in this chapter. Between-PSU variance is
estimated by subtracting the within-PSU variance from the
total variance. Due to variation of the variance estimates,
the between-PSU variance is sometimes negative. The far
right two columns of Table 14–2 show the percentage
within-PSU variance and the percentage between-PSU
variance in the total variance estimate.
For all characteristics shown in Table 14–2, the proportion of the total variance due to sampling housing units
within PSUs (within-PSU variance) is larger than that due
to sampling a subset of NSR PSUs (between-PSU variance). In fact, for most of the characteristics shown, the
within-PSU component accounts for over 90 percent of the
total variance. For civilian labor force and not in labor force
characteristics, almost all of the variance is due to sampling housing units within PSUs. For the total and White
employed in agriculture, the within-PSU component still
14–6
Table 14–2. Components of Variance for FSC Monthly Estimates
[Monthly averages: July - Dec. 1995]
FSC1 estimate
(x 106)
Standard error
(x 105)
Coefficient of
variation
(percent)
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.30
5.31
1.57
1.15
1.35
1.38
1.19
0.68
0.63
0.57
Employed - Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.47
3.23
0.10
0.60
0.29
Employed - Nonagriculture. . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Civilian-noninstitutional population
16 years old and over
Percent of total variance
Within
Between
1.89
2.24
4.32
5.51
4.25
98.4
98.1
100.4
94.2
98.7
1.6
1.9
−0.4
5.8
1.3
1.49
1.43
0.19
0.73
0.28
4.30
4.42
18.52
12.27
9.50
63.2
66.1
105.9
83.4
97.0
36.8
33.9
−5.9
16.6
3.0
122.34
104.05
13.29
10.72
6.40
3.34
3.05
1.30
1.43
0.96
0.27
0.29
0.98
1.34
1.50
92.8
94.4
100.3
86.3
108.1
7.2
5.6
−0.3
13.7
−8.1
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
133.11
112.58
14.97
12.47
8.04
2.89
2.61
1.18
1.09
0.98
0.22
0.23
0.79
0.87
1.22
99.0
99.3
99.9
99.2
103.5
1.0
0.7
0.1
0.8
−3.5
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65.96
54.67
8.37
6.31
6.61
2.89
2.61
1.18
1.09
1.00
0.44
0.48
1.41
1.73
1.52
99.0
99.3
99.9
99.2
101.5
1.0
0.7
0.1
0.8
−1.5
1
First- and second-stage combined (FSC).
Hispanics may be of any race.
2
accounts for 60 to 70 percent of the total variance, while
the between-PSU component accounts for the remaining
30 to 40 percent. The between-PSU component is also
larger (between 10 and 20 percent) for employed in
agriculture, Hispanic origin, and employed in nonagriculture, Hispanic origin than for other characteristics shown in
the table.
Because the FSC estimate of total civilian labor force
and total not in labor force must add to the independent
population controls (which are assumed to have no variance), the standard errors and variance components for
these estimated totals are the same. They are not the same
for the teenage category because the estimates do not
completely converge to the control totals during the secondstage ratio adjustment.
TOTAL VARIANCES AS AFFECTED BY
ESTIMATION
Table 14–3 shows how the separate estimation steps
affect the variance of estimated levels by presenting ratios
of relative variances. It is more instructive to compare
ratios of relative variances than the variances themselves,
since the various stages of estimation can affect both the
level of an estimate and its variance (Hanson, 1978; Train,
Cahoon, and Makens, 1978). The unbiased estimate uses
the baseweight with special weighting factors applied. The
noninterview estimate includes the baseweights, the special weighting adjustment, and the noninterview adjustment. The first- and second-stage ratio adjustments are
used in the estimate for the first- and second-stage combined (FSC).
In Table 14–3, the figures for unemployed show, for
example, that the relative variance of the FSC estimate of
level is 3.590 x 10-4 (equal to the square of the coefficient
of variation in Table 14–2). The relative variance of the
unbiased estimate for this characteristic would be 1.06
times as large. If the noninterview stage of estimation is
also included, the relative variance is only 1.05 times the
size of the relative variance for the FSC estimate of level.
Including the first stage of estimation raises the relative
variance factor to 1.13. The relative variance for total
unemployed after applying the second stage without the
first stage is about the same as the relative variance that
results from applying the first- and second-stage adjustments.
The relative variance as shown in the last column of this
table illustrates that the first-stage ratio adjustment has
little effect on the variance of national level characteristics
in the context of the overall estimation process. The
first-stage adjustment was implemented in order to reduce
variances of state-level estimates. Its actual effect on these
state-level estimates has not yet been examined.
The second-stage adjustment, however, appears to
greatly reduce the total variance, as intended. This is
14–7
Table 14–3. Effects of Weighting Stages on Monthly Relative Variance Factors
[Monthly averages: July - Dec. 1995]
Relative variance factor1
Relative
variance of FSC
estimate of level
(x 10–4)
Unbiased
estimator
NI2
NI & FS3
NI & SS4
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.590
5.004
18.626
30.347
18.092
1.06
1.11
1.30
1.14
1.13
1.05
1.11
1.28
1.14
1.13
1.13
1.16
1.28
1.19
1.14
1.00
1.00
1.00
1.00
0.99
Employed - Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18.460
19.514
342.808
150.548
90.235
0.98
0.98
1.03
1.01
1.08
0.97
0.97
1.02
0.98
1.08
1.04
1.03
1.04
1.06
1.10
0.96
0.96
1.01
0.95
1.00
Employed - Nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.075
0.086
0.957
1.783
2.264
4.71
5.54
6.73
3.19
2.03
4.47
5.44
6.60
3.18
2.00
6.85
7.55
6.61
3.27
2.05
0.96
0.97
1.00
0.95
1.00
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.047
0.054
0.623
0.764
1.497
7.28
8.96
9.87
7.03
2.69
6.84
8.74
9.62
6.95
2.64
10.98
12.33
9.64
7.41
2.74
0.99
0.99
1.01
0.99
0.99
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.192
0.228
1.994
2.984
2.312
2.79
3.22
4.21
2.99
2.04
2.67
3.16
4.11
2.92
2.03
4.15
4.50
3.88
3.09
2.23
0.99
0.99
1.01
0.99
1.00
Civilian-noninstitutional population
16 years old and over
Unbiased estimator with–
1
Relative variance factor is the ratio of the relative variance of the specified level to the relative variance of the FSC level.
NI = Noninterview.
3
FS = First-stage.
4
SS = Second-stage.
5
Hispanics may be of any race.
2
especially true for characteristics that comprise high proportions of age, sex, or race/ethnicity subclasses, such as
White, Black, or Hispanic persons in the civilian labor force
or employed in nonagricultural industries. Without the
second stage, the relative variances of these characteristics would be 5, 6, or even 11 to 12 times as large. For
smaller groups, such as unemployed and employed in
agriculture, the second-stage adjustment does not have as
dramatic an effect.
After the second-stage ratio adjustment, a composite
estimator is used to improve estimates of month-to-month
change by taking advantage of the 75 percent of the total
sample that continues from the previous month (see Chapter 10). Table 14–4 compares the variance and relative
variance of the composited estimates of level to those of
the FSC estimates. For example, the estimated variance of
the composited estimate of unemployed persons of Hispanic origin is 3.659 x 109. The variance factor for this
characteristic is 0.92, implying that the variance of the
composited estimate is 92 percent of the variance of the
estimate after the first and second stages. The relative
variance, which takes into account the estimate of the
number of people with this characteristic, is 0.94 times the
size of the relative variance of the FSC estimate. The two
factors are similar for most characteristics, indicating that
compositing tends to have a small effect on the level of
most estimates.
DESIGN EFFECTS
Table 14-5 shows the design effects for the total variance for selected labor force characteristics. A design
effect (deff) is the ratio of the variance from complex
sample design or a sophisticated estimation method to the
variance of a simple random sample design. The design
effects in this table were computed by solving equation
14.2 for deff and replacing N/n in the formula with an
estimate of the national sampling interval. Estimates of P
and Q were obtained from the 6 months of data.
For unemployed, the design effect is 1.314 for the uncomposited (FSC) estimate and 1.229 for the composited
estimate. This means that, for the same number of sample
cases, the design of the CPS (including the sample selection, weighting, and compositing) increases the variance
by about 23 percentage points over the variance of
an unbiased estimate based on a simple random sample.
On the other hand, for the civilian labor force the design of
the CPS decreases the variance by about 24 percentage points. Note that the design effects for composited
estimates are generally lower than those for the FSC
estimates indicating again the tendency of the compositing
to reduce the variance of most estimates.
14–8
Table 14–4. Effect of Compositing on Monthly Variance and Relative Variance Factors
[Monthly averages: July - Dec. 1995]
Variance of composited
estimate of level
(x 109)
Variance factor1
Relative variance
factor2
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17.690
13.003
4.448
3.659
3.279
0.93
0.93
0.97
0.92
1.00
0.94
0.94
1.00
0.94
1.01
Employed - Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20.789
18.781
0.302
4.916
0.713
0.93
0.93
0.85
0.91
0.92
0.94
0.93
0.95
0.88
0.93
Employed - Nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94.359
79.145
14.489
18.466
8.508
0.85
0.85
0.85
0.90
0.92
0.84
0.85
0.85
0.90
0.92
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
69.497
58.329
12.037
10.448
8.607
0.83
0.85
0.86
0.88
0.89
0.83
0.85
0.87
0.88
0.89
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
69.497
58.329
12.037
10.448
9.004
0.83
0.85
0.86
0.88
0.89
0.83
0.85
0.85
0.88
0.88
Civilian-noninstitutional population
16 years old and over
1
Variance factor is the ratio of the variance of a composited estimate to the variance of an FSC estimate.
Relative variance factor is the ratio of the relative variance of a composited estimate to the relative variance of an FSC estimate.
3
Hispanics may be of any race.
2
Note: Composite formula constants used: K = 0.4 and A = 0.2.
Table 14–5. Design Effects for Total Monthly Variances
[Monthly averages: July - Dec. 1995]
Design effects for total variance
Civilian-noninstitutional population
16 years old and over
After first and
second stages
After compositing
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.314
1.318
1.424
1.689
1.183
1.229
1.227
1.403
1.574
1.191
Employed - Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.153
3.091
1.680
4.370
1.278
2.958
2.875
1.516
3.903
1.188
Employed - Nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.144
0.904
0.659
0.976
0.723
0.966
0.770
0.564
0.880
0.664
Civilian labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.913
0.672
0.487
0.491
0.606
0.760
0.576
0.421
0.433
0.540
Not in labor force. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.913
0.829
0.841
0.939
0.763
0.760
0.709
0.723
0.824
0.679
1
Hispanics may be of any race.
14–9
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U.S. Dept. of Labor, Bureau of Labor Statistics, Employment and Earnings, 42, p.152, Washington, DC: Government Printing Office.
Valliant, R. (1987), ‘‘Generalized Variance Functions in
Stratified Two-Stage Sampling,’’ Journal of the American
Statistical Association, 82, pp. 499-508.
Wolter, K. (1984), ‘‘An Investigation of Some Estimators
of Variance for Systematic Sampling,’’ Journal of the
American Statistical Association, 79, pp. 781-790.
Wolter, K. (1985), Introduction to Variance Estimation, New York: Springer-Verlag.
15–1
Chapter 15.
Sources and Controls on Nonsampling Error
INTRODUCTION
Nonsampling error can enter the survey process at any
point or stage, and many of these errors are not readily
identifiable. Nevertheless, the presence of these errors can
affect both the bias and variance components of the total
survey error. The effect of nonsampling error on the
estimates is difficult to measure accurately. For this reason,
the most appropriate strategy is to examine the potential
sources of nonsampling error and take steps to prevent
these errors from entering the survey process. This chapter
discusses the various sources of nonsampling error and
the measures taken to control their presence in the Current
Population Survey (CPS).
Many sources of nonsampling error were studied and
well-charted in the pre-January 1994 CPS (e.g., see Brooks
and Bailar, 1978; McCarthy, 1978), but the full extent of
nonsampling error in the post-January 1994 CPS is not
nearly so well examined. It is also unclear how much
previous research on nonsampling error in the CPS is
applicable to the redesigned instrument with computerized
data collection. Although the intent of the new questionnaire and automation was to reduce nonsampling error,
new sources of error may emanate from these methodological advances. Some potential sources of nonsampling
error can be investigated with the available CPS and
reinterview data, but there is only about 2 years of data
available for analyses and comparisons at the time of this
writing, and this may not be sufficient for a full analysis.
Nevertheless, it is clear that there are two main types of
nonsampling error in the CPS. The first is error imported
from other frames or sources of information, such as
Decennial Census omissions. The second type is considered preventable, such as when the sample is not completely representative of the intended population, withinhousehold omissions, respondents not providing true answers
to a questionnaire item, or errors produced during the
processing of the survey data.
Chapter 16 discusses the presence of CPS nonsampling error, but not the effect of the error on the estimates.
The present chapter, however, focuses on the sources and
operational efforts used to control the occurrence of error in
the survey processes. Each section discusses a procedure
aimed to reduce either coverage, nonresponse, response,
or data processing errors. Despite the effort to treat each
control as a separate entity, they nonetheless affect the
survey in a general way. For example, training, even if
focused on a specific problem, can have broadreaching
total survey effects.
An understanding of the sources and causes of nonsampling error is necessary to design procedures to control the
effects of these errors on the estimates. An important
source of nonsampling error that is difficult to understand
and control arises out of the interaction of the people
involved in the survey. The methods by which administrators, analysts, programmers, and field personnel interact
and exchange information are a potential source of nonsampling error. One attempt to reduce this source of
nonsampling error is demonstrated by the use of Deming’s
quality management procedures in the 1990 sample redesign (Waite, 1991). It emphasizes the relationship between
process design and quality improvement. It is a nonbureaucratic approach that has all involved persons working
together toward a common goal. Each person is accountable and responsible for the verification of the consistency,
reliability, and accuracy of his/her output before it is used
as input to a succeeding process or stage. The quality of all
previous processes affects the quality of all succeeding
processes. Communication across all stages is enhanced
by the inclusion of the entire staff involved in the operations, planning, and implementation of the processes up-front,
even before the writing of specifications.
Included in the notions of interaction and exchange of
information across all personnel levels and processes are
feedback, continuous improvement, the introduction of
automation and computerization, and training. Feedback,
when accompanied by corrective measures, is vital and
must flow in all directions between the field representative,
the regional office, headquarters, and the data preparation
office in Jeffersonville, Indiana. (Focus groups are an
example of an effective source of feedback.) Continuous
improvement in every process and stage of the CPS is a
requirement, not an option. Also, automation and computerization are major factors in the control of nonsampling
error, greatly improving uniformity of operations, as well as
data access and control. The degree of automation is
continuously increasing, thus affecting the amount and
allocation of responsibilities. Finally, training is a continuing
task in each regional office, the Jeffersonville office, and
headquarters, and it is a major factor in controlling the
occurrence of nonsampling error. (See Appendix F for a
more detailed discussion on training procedures in the
regional offices.)
This chapter deals with many sources and many controls for nonsampling error. It covers coverage error, error
from nonresponse, response error, and processing errors.
Many of these types of errors interact, and they can occur
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anywhere in the survey process. Although it is believed that
some of the nonsampling errors are small, their full effects
on the survey estimates are unknown. The CPS attempts
to prevent such errors from entering the survey process or
tries to keep them as small as possible.
SOURCES OF COVERAGE ERROR
Coverage error exists when a survey does not completely represent the population of interest. When conducting a sample survey, a primary goal is to give every unit (for
example, person or housing unit) in the target universe a
known probability of being selected into the sample. When
this occurs, the survey is said to have 100 percent coverage. This is rarely the case, however, since errors can
enter the system during almost any phase of the survey
process. A bias in the survey estimates results if characteristics of units erroneously included or excluded from the
survey differ from those correctly included in the survey.
Historically in the CPS, the net effect of coverage errors
has been a loss in population (resulting from undercoverage).
The primary sources of CPS coverage error are:
1. Frame omission. Erroneous frame omission of housing units occurs when the list of addresses is incomplete. This can occur in all of the four sampling frames
(i.e., unit, permit, area, and group quarters; see Chapter 3). Since these erroneously omitted housing units
cannot be sampled, undercoverage of the target population results. Reasons for frame omissions are:
• Census Address List: The census address list may
be incomplete or some units are not locatable. This
occurs when the census lister fails to canvass an
area thoroughly or misses units in multiunit structures.
• New Construction: Some areas of the country are
not covered by building permit offices and are not
recanvassed (unit frame) to pick up new housing.
New housing units in these areas have zero probability of selection.
• Mobile Homes: Mobile homes that move into areas
that are not recanvassed (unit frame) also have a
zero probability of selection.
2. Misclassification of housing units. Erroneous frame
inclusions of housing units occur when housing units
outside an appropriate area boundary are misclassified as inside the boundary. Other erroneous inclusions occur when a single unit is recorded as two units
through faulty application of the housing unit definition.
Since erroneously included housing units can be
sampled, and if the errors are not detected and corrected, overcoverage of the target population results.
Misclassification also occurs when an occupied
housing unit is treated as vacant.
3. Within-housing unit omissions. Undercoverage of
persons can arise from failure to list all usual residents
of a housing unit on the questionnaire and from
misclassifying a household member as a nonmember.
4. Within-housing unit inclusions. Overcoverage can
occur because of the erroneous inclusion of persons
on the roster for a housing unit. For instance, persons
with a usual residence elsewhere are treated as
members of the sample housing unit.
Other sources of coverage error are omission of homeless persons from the frame, unlocatable addresses from
building permit lists, conversion of nonresidential to residential units, and missed housing units due to the start
dates for the sampling of permits.1 Newly constructed
group quarters are another possible source of undercoverage. If noticed on the permit lists, the general rule is to
purposely remove these group quarters; if not noticed and
the field representative discovers a newly constructed
group quarters, the case is stricken from the roster of
sample addresses and never visited again for CPS. For
example, dormitories are group quarters in the 1990 decennial census and are included in the group quarters frame.
They are picked up in the other frames, not in the permit
frame.
The sampling frames are designed to limit missed
housing units in the census frame from affecting coverage
of the CPS frames (see Chapter 3). Through various
studies, it has been determined that the number of housing
unit misses is small; this is because of the way the
sampling frames are designed and because of the routines
in place to ensure that the listing of addresses is performed
correctly. A measure of potential undercoverage is the
coverage ratio which incorporates the completeness of the
coverage because of frame omissions, misclassification of
housing units, and within-housing unit omissions and inclusions. For example, the coverage ratio for the total population, at least 16 years of age, is approximately 92 percent
(January 1997). In general, there is low coverage for
Blacks (about 83 percent), with Black males of ages 25-34
having the lowest coverage. Females have higher coverage. See Chapter 16 for further discussion of coverage
error.
CONTROLLING COVERAGE ERROR
This section focuses on those processes during the
sample design and sample preparation stages which aim
to control housing unit omissions. Other sources of undercoverage can be viewed as response error and are described
in a later section of this chapter.
1
Sampling of permits began with those issued in 1989; the month
varied depending on the size of structure and region. Housing units whose
permits were issued before the start month in 1989 and not built by the
time of the census may be missed.
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Sample Design
The CPS sample selection is coordinated with the
samples of nine other demographic surveys. These surveys undergo the same verification processes, so the
discussion in the rest of this section is in no way unique to
the CPS.
Sampling verification basically consists of testing and
production. They are different in that the intent of the
testing phase is to design the processes better to improve
quality, and the intent of the production phase is to verify or
inspect quality to improve the processes. Both phases are
coordinated across the various surveys. Sampling intervals, expected sample sizes, and the premise that a
housing unit is selected for only one survey for 10 years are
continually verified.
The testing phase tests and verifies the various sampling programs before any sample is selected, ensuring
that the programs work individually and collectively. Smaller
data sets with unusual observations test the performance
of the system in extreme situations. For example, unusual
circumstances encountered in the 1990 decennial census
are used to test the 1990 CPS sampling procedures. These
circumstances include PSUs with no sample, crews of
maritime vessels, group quarters, and Indian Reservations.
Programs are then written to verify that procedures are in
place to handle the problems. Also, actual data from the
1990 decennial census are used to test and verify the
sampling processes.
The production phase of sampling verification treats
output verification, especially expected sample sizes, focusing on whether the system ran successfully on actual data.
The following are illustrations and do not represent any
particular priority or chronology:
1. PSU probabilities of selection are verified; for example,
their sum should be one.
2. Edits are done on file contents, checking for blank
fields, out-of-range data, etc.
Listing sheet review. The listing sheet review is a check
on each month’s listings to keep the nonsampling error as
low as possible. Its aim is to ensure that the interviews will
be conducted at the correct units and that all units have
one and only one chance of selection. This review also
plays a role in the verification of the sampling, subsampling, and relisting. At present, the timing of the review is
such that not all errors detected in the listing sheet review
are able to be corrected in time for incorporation into CPS.
However, automation of the review is a major advancement
towards improving the timing of the process and, thus, a
more accurate CPS frame.
Listing sheet reviews for the unit, permit, area, and
group quarters samples are performed in varying degrees
by both the regional offices and the Jeffersonville office.
The fact that these reviews are performed across different
parts of the Census Bureau organization is, in itself, a
means of controlling nonsampling error.
In terms of the reviews performed by the regional
offices, of utmost importance is the review of field representative explanations made in the remarks column or
footnotes section of the listing sheet. Other processes that
the regional offices follow to verify and correct the listing
sheets for samples from each type of frame follow.
After a field representative makes an initial visit either to
a multiunit address from a unit frame or to a sample from a
permit frame, the Unit/Permit Listing Sheet is reviewed.
This review occurs the first time the basic address or
sample from a permit frame is in sample for any survey.
However, if major changes occur at an address on a
subsequent visit, a review of the revised listing is done. If
there is evidence that the field representative encountered
a special or unusual situation, the materials are compared
to the instructions in the Listing and Coverage Manual for
Field Representatives (see Chapter 4) to ensure that the
situation was handled correctly. Depending on whether it is
a permit frame sample or a multiunit address from a unit
frame, the following are verified:
3. When applicable, the files are coded to check for
consistency; for example, the number of PSUs ‘‘in’’
should equal the number of PSUs ‘‘out’’ at PSU
selection.
1. Were correct entries made on the listing sheet when
either fewer or more units were listed than expected?
4. Information is centralized; for example, the sampling
rates for all states and across all surveys are located in
one parameter file.
3. Were new listing sheets prepared?
5. As a form of overall consistency verification, the output
at several stages of sampling is compared to that of
the former CPS design.
5. Was an extra unit discovered?
2. Was CPS the first survey to interview at the multiunit
address?
4. Were no units listed?
6. Was a sample unit added without a serial number?
7. Did the unit designation change?
Sample Preparation
Sample preparation activities, in contrast to those of
sample design, are ongoing. The listing sheet review and
check-in of sample are monthly production processes, and
the listing check is an ongoing field process.
8. Was a sample unit demolished or condemned?
Errors and omissions are corrected and brought to the
attention of the field representative. This may involve
contacting the field representative for more information.
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The regional office review of Area and Group Quarters
Listing Sheets is performed on newly listed or updated
samples from area or group quarters frames. Basically, this
review is for completeness of identification.
As stated previously, listing sheet reviews are also
performed by the Jeffersonville office. The following are
activities involved in the reviews for the unit sample:
1. Use the mailout control to check-in listing sheets from
the regional offices and followup any that are missing.
2. If subsampling was done, review for accuracy.
3. If any large multiunit addresses were relisted, review
for accuracy.
4. If there were any address changes, key the changes
into the address updates system for future samples at
the same basic address.
5. Keep tallies by regional office and survey of when any
of situations 2 through 4 above were encountered;
provide headquarters with these tallies on a quarterly
basis.
In terms of the review of listing sheets for permit frame
samples, Jeffersonville checks to see whether the number
of units is more than expected and whether there are any
changes to the address. If there are more units than
expected, Jeffersonville determines whether the additional
units already had a chance of selection. If so, the regional
office is contacted with instructions for updating the listing
sheet. If there are any address changes, Jeffersonville
provides updates to headquarters for potential future samples
at the same basic address.
For the area and group quarters sample, the listing
sheets remain in the regional offices. However, Jeffersonville receives a summary of the sampling that was done by
each regional office ensuring that, given the sampling rate,
the regional office sampled the appropriate number of
units. If not, the regional office is contacted and corrections
are made.
The majority of listing sheets are found without any
inaccuracies during the regional office and Jeffersonville
reviews across all frame types. When errors or omissions
are detected, they usually occur in batches, signifying a
misinterpretation of instructions by a regional office or a
newly hired field representative. Therefore, because of
these low error rates during the listing sheet review, the
intent and value of this review are being revisited.
Check-in of sample. Depending on the sampling frame
and mode of interview, the monthly process of check-in of
sample can occur many times as the sample cases progress
through the regional offices, the Jeffersonville office, and
headquarters. Check-in of sample describes the processes
by which the regional offices verify that the field representatives receive all the sample cases that they are supposed
to and that all are completed and returned. Since the CPS
is now conducted entirely by either computer assisted
personal or telephone interview, a closer control of the
tracking of a sample case exists than was ever possible.
Control systems are in place to control and verify the
sample count. The regional offices use these systems to
control the sample during the period when it is active, and
all discrepancies must be resolved before the office is able
to certify that the workload in the database is correct. Since
these systems aid in the balancing of the workload, time
and effort can then be spent elsewhere.
Listing check. The purpose of the regional offices’ listing
check is to provide assurance that quality work is done on
the listing operation and to provide feedback to the field
representatives about their performance, with possible
retraining when a field representative misunderstands or
misapplies a listing concept. In the field, it checks a sample
of each field representative’s work to see whether he/she
began the listing of units at the correct location, listed the
units correctly, stayed in the appropriate geographic boundaries, and included all the units. Since it is not a part of the
monthly production cycle and it can occur either before or
after an interview, its intent is not to capture and correct
data. Rather, it is an attempt, over time, to counteract any
misunderstanding of listing and coverage procedures.
Since January 1993, the listing check has been a
separate operation from reinterview; it is generally combined with some other personal visit field operation, such
as observation. To avoid a bias in the designation of the
units to be interviewed, the listing is performed without any
knowledge of which units will be in the interview or reinterview. A listing check is performed only on area frame
samples for field representatives who list or update. These
field representatives are randomly checked once during
the fiscal year, and depending on how close assignments
are to each other, at least two assignments are checked to
better evaluate the field representative’s listing skills. The
check of the unit frame sample was dropped when address
samples were converted to samples from the unit frame in
the 1990 Sample Redesign. This was done since little of
the unit frame sample requires listing. Also, because permit
and group quarters samples constitute such a small part of
the universe, no formal listing check is done for sample in
these frames.
SOURCES OF ERROR DUE TO NONRESPONSE
There are a variety of sources of nonresponse error in
the CPS. Noninterviews could include housing units that
are vacant, not yet built, demolished, or are not residential
living units; however, these types of noninterviews are not
considered to be nonresponses since they are out-ofscope and not eligible for interview. (See Chapter 7 for a
description of the types of noninterviews.) Nonresponse
error does occur when households that are eligible for
interview are not interviewed for some reason: a respondent refuses to participate in the survey, is incapable of
15–5
completing the interview, or is not available or not contacted by the interviewer during the survey period, perhaps
due to work schedules or vacation. These household
noninterviews are called Type A noninterviews.
There are additional kinds of nonresponse in the CPS.
Individual persons within the household may refuse to be
interviewed, resulting in person nonresponse. Person nonresponse has not been much of a problem in the CPS
because any responsible adult in the household is able to
report for other persons in the household as a proxy
reporter. Also, panel nonresponse exists when those persons who live in the same household during the entire time
they are in the CPS sample may not agree to be interviewed each of the 8 months. Thus, panel nonresponse
can be important if the CPS data are used longitudinally.
Finally, some respondents who complete the CPS interview may be unable or unwilling to answer specific questions resulting in item nonresponse. Imputation procedures
are implemented for item nonresponse. However, because
there is no way of ensuring that the errors of item imputation will balance out, even on an expected basis, item
nonresponse also introduces potential bias into the estimates.
For a sense of the magnitude of the error due to
nonresponse, the household noninterview rate was 6.34
percent in January 1997. For item nonresponse, it is
interesting to note allocation rates of 0.3 percent for the
characteristic of Labor Force Status, 1.4 percent for Race,
and 1.7 percent for Occupation, all using January 1997
weighted data. (See Chapter 16 for discussion on various
quality indicators of nonresponse error.)
CONTROLLING ERROR DUE TO
NONRESPONSE
Field Representative Guidelines2
Response/nonresponse rate guidelines have been developed for field representatives to help ensure the quality of
the data collected. Maintaining high response rates is of
primary importance, and response/nonresponse guidelines
have been developed with this in mind. These guidelines,
when used in conjunction with other sources of information,
are intended to assist supervisors in identifying field representatives needing performance improvement. A field representative whose response rate, household noninterview
rate (Type A), or minutes per case fall below the fully
acceptable range based on one quarter’s work is considered in need of additional training and development, and
the CPS supervisor takes appropriate remedial action.
National and regional response performance data are also
provided to permit the regional office staff to judge whether
their activities are in need of additional attention.
2
See Appendix F for a detailed discussion, especially in terms of the
performance evaluation system.
Summary Sheets
Another way to control nonresponse error is the production and review of summary sheets. Although produced by
headquarters after the release of the monthly data products, they are used to detect changes in historical response
patterns. Also, since they are distributed throughout headquarters and the regional offices, other indications of data
quality and consistency can be focused upon. Contents of
selected summary report tables are as follows: noninterview rates by regional office; monthly comparisons to prior
year; noninterview-to-interview conversion rates; resolution status of computer assisted telephone interview cases;
interview status by month-in-sample; daily transmittals;
and coverage ratios.
Headquarters and Regional Offices Working as
a Team
As detailed in a Methods and Performance Evaluation
Memorandum (Reeder, 1997), the Census Bureau and the
Bureau of Labor Statistics formed an interagency work
group to examine CPS nonresponse in detail. One goal
was to share possible reasons and solutions for the
declining CPS response rates. A list of 31 questions was
prepared for the regional offices to help aid in the understanding of CPS field operations, to solicit and share the
regional offices’ views on the causes of the increasing
nonresponse rates, and to evaluate methods to decrease
these rates. All of the answers provide insight on CPS
operations that may affect nonresponse and followup procedures for household noninterviews, but a listing of a few
will suffice:
• The majority of regional offices responded that there is
written documentation of the followup process for CPS
household noninterviews;
• The standard process was that a field representative
must let the regional office know about a possible
household noninterview as soon as possible;
• Most regions attempt to convert confirmed refusals under
certain circumstances;
• All regions provide monthly feedback to their CPS field
representatives on their household noninterview rates;
and
• About half of the offices responded that they provide
specific regional based training/activities for field representatives on converting or avoiding household noninterviews.
Response to one question deserves special attention:
Most offices use letters in a consistent manner for followup
of noninterviews. Most regional offices also include informational brochures tailored to the respondent with the
letters.
15–6
SOURCES OF RESPONSE ERROR
The survey interviewer asks a question and collects a
response from the respondent. Response error exists if the
response is not the true answer. Reasons for response
error include:
• The respondent misinterprets the question, tries to look
better by inflating or deflating the response, does not
know the true answer and guesses (for example, recall
effects), or chooses a response randomly;
• The interviewer reads the question incorrectly, does not
follow the appropriate skip pattern, misunderstands or
misapplies the questionnaire, or records the wrong answer;
• The data collection modes (for example, personal visit
and telephone) elicit different response rates, as well as
the frequency of interview. Studies have shown that
response rates generally decline as the length of time in
survey increases; and
• The questionnaire does not elicit correct responses, due
to a nonuser-friendly format, complicated skip patterns,
and difficult coding procedures.
Thus, response error can arise from many sources. The
survey instrument, the mode of data collection, the interviewer, and the respondent are the focus of this section3,
as well as their interactions as discussed in The Reinterview Program.
In terms of magnitude, measures of response error are
obtainable through the reinterview program; specifically,
Chapter 16 discusses the index of inconsistency. It is a
ratio of the estimated simple response variance to the
estimated total variance, arising from sampling and simple
response variance. When identical responses are obtained
from trial to trial, both the simple response variance and the
index of inconsistency are zero. Theoretically, the index
has a range of 0 to 100. For example, the index of
inconsistency for the labor force characteristic of unemployed for February through July 1994 is considered moderate at 30.8.
CONTROLLING RESPONSE ERROR
The Survey Instrument4
The survey instrument involves the CPS questionnaire,
the computer software that runs the questionnaire, and the
mode by which the data are collected. The modes are
3
Most discussion in this section is applicable whether the interview is
conducted via computer assisted personal interview, telephone interview
by the field representative or a centralized telephone facility, or computer
assisted telephone interview.
4
Many of the topics in this section are presented in more detail in
Chapter 6.
personal visits or telephone calls made by field representatives and telephone calls made by interviewers at centralized telephone centers. Regardless of the mode, the
questionnaire and the software are basically the same
(See Chapter 7.)
Software. Changes in the early 1990s resulted in the use
of computer assisted interviewing technology in the CPS.
Designed in an automated environment, this technology
allows very complex skip patterns and other procedures
which combine data collection, data input, and a degree of
in-interview consistency editing into a single operation.
This technology provides an automatic selection of
questions for each interview. The screens display response
options, if applicable, and information about what to do
next. The interviewer does not have to worry about skip
patterns, with the possibility of error. Appropriate proper
names, pronouns, verbs, and reference dates are automatically filled into the text of the questions. If there is a refusal
to answer a demographic item, that item is not asked again
in later interviews; rather, it is longitudinally allocated. This
exhibits the trade-off of nonsampling error existing for the
item versus the possibility of a total noninterview. The
instrument provides opportunities for the field representative to review and correct any incorrect/inconsistent information before the next series of questions is asked,
especially in terms of the household roster. In later months,
the instrument passes industry and occupation information
forward to be verified and corrected. In addition to reducing
response and interviewer burden, this also avoids erratic
variations in industry and occupation codes among pairs of
months for people who have not changed jobs, but describe
their industry and occupation differently in the 2 months.
The questionnaire. Beginning in January 1994, the CPS
used a questionnaire in which the wording was different
from what was collected previously for almost every labor
force concept. One of the objectives in redesigning the
CPS questionnaire was to reduce the potential for response
error in the questionnaire-respondent-interviewer interaction and to improve measurement of CPS concepts. The
approaches used to lessen the potential for response error
(i.e., enhanced accuracy) were: shorter and clearer question wording, splitting complex questions into two or more
questions, building concept definitions into question wording, reducing reliance on volunteered information, explicit
and implicit strategies for the respondent to provide numeric
data, and the use of precoded response categories for
open-ended questions. Although they were also used with
the pencil and paper questionnaire, interviewer notes recorded
at the end of the interview are critical to obtaining reliable
and accurate responses.
Mode of data collection. As stated in Chapters 7 and 16,
the first and fifth months’ interviews are done in person
whenever possible, while the remaining interviews may be
conducted via telephone either by the field interviewer or
15–7
an interviewer from a centralized telephone facility. Although
each mode has its own set of performance guidelines that
must be adhered to, similarities do exist. The controls
detailed in The Interviewer of this section, and Error Due to
Nonresponse, which are mainly directed at personal visits,
are basically valid for the calls made from the centralized
facility via the supervisor’s listening in.
Continuous testing and improvements. Experience gained
during the CPS redesign and research on questionnaire
design has assisted in the development of methods for
testing new or revised questions for the CPS. In addition to
reviewing new questions to ensure that they will not
jeopardize the collection of basic labor force information
and to determine whether the questions are appropriate
additions to a household survey about the labor force, the
wording of new questions are tested to gauge whether
respondents are correctly interpreting the questions. Chapter 6 provides an extensive listing of the various methods of
testing and the Census Bureau developed a set of protocols for pretesting demographic surveys.
There is also interest in improving existing questions.
The ‘‘don’t know’’ and refusal rates for specific questions
are monitored, inconsistencies caused by instrument-directed
paths through the survey or instrument-assigned classifications are looked for during the estimation process, and
interviewer notes recorded at the conclusion of the interview are reviewed. Also, focus groups with CPS interviewers and supervisors are periodically conducted.
Despite the benefits of adding new questions and improving existing ones, changes in the CPS are approached
cautiously until the effects are measured and evaluated.
When possible, methods to bridge differences caused by
changes or techniques to avoid the disruption of historical
series are included in the testing. In fact, for the 18 months
prior to the implementation of the 1994 redesigned CPS, a
parallel survey was conducted using the new methodology
and, for the first 5 months after the redesigned survey was
introduced, a parallel survey was conducted using the
unrevised procedures. Results from the parallel survey
were used to anticipate the effect the changes would have
on the survey estimates, nonresponse rates, etc. (Kostanich and Cahoon, 1994; Polivka, 1994; and Thompson,
1994.) Comparable testing and parallel surveys were used
for previous revisions to the questionnaire.
The Interviewer
Interviewer training, observation, monitoring, and evaluation are all methods used to control nonsampling error,
arising from both the frame and data collection. For further
discussion of field representative guidelines, see this chapter’s Error Due to Nonresponse section and Appendix F.
Group training and home study are continuing tasks in
each regional office to control various nonsampling errors,
and they are tailored to the types of duties and length of
service of the interviewer. Observations, including those of
the listing check, are extensions of classroom training and
provide on-the-job training and on-the-job evaluation.
Field observation is one of the methods used by the
supervisor to check and improve the performance of the
field representative. It provides a uniform method for
assessing the field representative’s attitudes toward the job
and use of the computer and evaluating the field representative’s ability to apply CPS concepts and procedures
during actual work situations. There are three types of
observations: initial, general performance review, and special needs. Across all types, the observer stresses good
interviewing techniques: asking questions as worded and
in the order presented on the questionnaire, adhering to
instructions on the instrument and in the manuals, knowing
how to probe, recording answers in the correct manner and
in adequate detail, developing and maintaining good rapport with the respondent conducive to an exchange of
information, avoiding questions or probes that suggest a
desired answer to the respondent, and determining the
most appropriate time and place for the interview. The
emphasis is on correcting habits which interfere with the
collection of reliable statistics.
The Respondent: Self Versus Proxy
The CPS Interviewing Manual (see Chapter 4) states
that any household member 15 years of age or older is
technically eligible to act as a respondent for the household. The field representative attempts to collect the labor
force data from each eligible individual; however, in the
interests of timeliness and efficiency, any knowledgeable
adult household member can provide the information. Also,
the survey instrument is structured so that every effort is
made to interview the same respondent every month. Just
over one-half of the CPS labor force data are collected by
self-response, and most of the remainder is collected by
proxy from a household respondent. The use of a nonhousehold member as a household respondent is only
allowed in certain limited situations; for example, the
household may consist of a single person whose physical
or mental health does not permit a personal interview.
As a caveat, there has been a substantial amount of
research into self versus proxy reporting, including research
involving CPS respondents. Much of the research indicates
that self-reporting is more reliable than proxy reporting,
particularly when there are motivational reasons for self
and proxy respondents to report differently. For example,
parents may intentionally ‘‘paint a more favorable picture’’
of their children than fact supports. However, there are
some circumstances in which proxy reporting is more
accurate, such as in responses to certain sensitive questions.
Interviewer/Respondent Interaction
Rapport with the respondent is a means of improving
data quality. This is especially true for personal visits, which
15–8
are required for months-in-sample one and five whenever
possible. By showing a sincere understanding and interest
in the respondent, a friendly atmosphere is created in
which the respondent can talk honestly and openly. Interviewers are trained to ask questions exactly as worded and
to ask every question. If the respondent misunderstands or
misinterprets a question, the question is repeated as
worded and the respondent is given another chance to
answer; probing techniques are used if a relevant response
is still not obtained. The respondent should be left with a
friendly feeling towards the interviewer and the Census
Bureau, clearing the way for future contacts.
The Reinterview Program - Quality Control and
Response Error5
One of the objectives of the reinterview program is to
evaluate individual field representative performance. Thus,
it is a significant part of quality control. It checks a sample
of the work of a field representative and identifies and
measures aspects of the field procedures which may need
improvement. It is also critical in the identification and
prevention of data falsification.
The reinterview program also provides a measure of
response error. Responses from first and second interviews at selected households are compared and differences are identified and analyzed. This helps to evaluate
the accuracy of the original survey results; as a byproduct,
instructions, training, and procedures are also evaluated.
SOURCES OF MISCELLANEOUS ERRORS
Data processing errors are one focus of this final
section. Their sources can include data entry, industry and
occupation coding, and methodologies for edits, imputations, and weighting. Also, the CPS Population Controls
are not error-free; a number of approximations or assumptions are used in their derivations. Other sources are
composite estimation and modeling errors which may arise
from, for example, seasonally adjusted series for selected
labor force data, and monthly model-based state labor
force estimates.
CONTROLLING MISCELLANEOUS ERRORS
Parallel Survey
Among specific processes that are used to control
nonsampling error in each of these sources, the parallel
survey mentioned in Response Error crosses several sources.
The 18 months of the parallel survey were used to test the
survey instrument and the development of the processing
5
Appendix G provides an overview of the design and methodology of
the entire Reinterview Program.
system. It ensured that the edits, imputations, and weighting program were working correctly. It often uncovered
related problems, such as incorrect skip-patterns. (The
Bureau of Labor Statistics was involved extensively in the
review of the edits.)
Industry and Occupation Coding Verification
To be ultimately accepted into the CPS processing
system, files containing records needing three-digit industry and occupation codes are electronically sent to the
Jeffersonville office for the assignment of these codes (see
Chapter 9). Once completed and transmitted back to
headquarters, the remainder of the production processing,
including edits, weighting, microdata file creation, and
tabulations can begin.
Using on-line industry and occupation reference materials, the Jeffersonville coder enters three-digit numeric
industry and occupation codes that represent the description for each case on the file. If the coder cannot determine
the proper code, the case is assigned a referral code,
which will later be coded by a referral coder. A substantial
effort is directed at the supervision and control of the quality
of this operation. The supervisor is able to turn the dependent verification setting on or off at any time during the
coding operation. (In the on mode, a particular coder’s
work is to be verified by a second coder. Additionally, a 10
percent sample of each month’s cases is selected to go
through a quality assurance system to evaluate each
coder’s work. The selected cases are verified by another
coder after the current monthly processing has been
completed. Upon completion of the coding and possible
dependent verification of a particular batch, all cases for
which a coder assigned at least one referral code must be
reviewed and coded by a referral coder.
Edits, Imputation, and Weighting
As detailed in Chapter 9, there are six edit modules:
household, demographic, industry and occupation, labor
force, earnings, and school enrollment. Each module establishes consistency between logically related items, assigns
missing values using relational imputation, longitudinal
editing, or cross-sectional imputation, and deletes inappropriate entries. Each module also sets a flag for each edit
step that can potentially affect the unedited data.
Consistency is one of the checks used to control nonsampling error. Are the data logically correct, and are the
data consistent within the month? For example, if a respondent says that he/she is a doctor, is he/she old enough to
have achieved this occupation? Are the data consistent
from the previous month and across the last 12 months?
The imputation rates should normally stay about the same
as the previous month’s, taking seasonal patterns into
account. Are the universes verified, and how consistent are
the interview rates? In terms of the weighting, a check is
15–9
made for zero or very large weights. If such outliers are
detected, verification and possible correction follow. Another
method to validate the weighting is to look at coverage
ratios which should fall within certain historical bounds. A
key working document used by headquarters for all of
these checks is a three-sheet tabulation of monthly summary statistics that highlights the six edit modules and the
weighting program for the past 12 months.
Modeling Errors
Extensive verification at the time of program development was done to ensure that it was working correctly.
Also, as part of the routine production processing at
headquarters and as part of the routine check-in of the data
by the Bureau of Labor Statistics, both compute identical
tables in composited and uncomposited modes. These
tables are then checked cell for cell to ensure that all
uncomposited cells across the two agencies and that all
composited cells across the two agencies are identical. If
so, and they always have been, the data are deemed to
have been computed correctly.
It may be hypothesized that although modeling may
indeed reduce some sampling and nonsampling errors,
other nonsampling error due to the misspecification of the
model may be introduced. Regardless, this final section
focuses on a few of the methods of nonsampling error
reduction, as applied to the seasonal adjustment programs, monthly model-based state labor force estimates,
and composite estimation for selected labor force data.
(See Chapter 10 for a discussion of these procedures.)
Changes that occur in a seasonally adjusted series
reflect changes other than those arising from normal
seasonal change; they are believed to provide information
about the direction and magnitude of changes in the
behavior of trend and business cycle effects. They may,
however, also reflect the effects of sampling and nonsampling errors, which are not removed by the seasonal
adjustment process. Research into the sources of these
irregularities, specifically nonsampling error, can then lead
to controlling their effects and even removal. The seasonal
adjustment programs contain built-in checks as verification
that the data are well-fit and that the modeling assumptions
are reasonable. These diagnostic measures are a routine
part of the output.
The processes for controlling nonsampling error during
the production of monthly model-based state labor force
estimates are very similar to those used for the seasonal
adjustment programs. Built-in checks exist in the programs
and, again, a wide range of diagnostics are produced that
indicate the degree of deviation from the assumptions.
CPS Population Controls
REFERENCES
The CPS composite estimator is used for the derivation
of most official CPS labor force estimates that are based
upon information collected every month from the full sample
(i.e., not collected in periodic supplements or from partial
samples.) As detailed in Chapter 10, it modifies the aggregated estimates without adjusting the weights of individual
sample records by combining the simple weighted estimate
and the composite estimate for the preceding month plus
an estimate of the change from the preceding to the current
month.
Total and state-level CPS population controls are developed by the Census Bureau independently from the collection and processing of CPS data. These monthly independent projections of the population are used for the
iterative, second-stage weighting of the CPS data. All of the
estimates start with the last census, with administrative
records and projection techniques providing updates. (See
Appendix D for a detailed discussion of the methodologies.)
As a means of controlling nonsampling error throughout
the processes, numerous internal consistency checks in
the programming are performed. For example, input files
containing age and sex detail are compared to independent files that give age and sex totals. Second, internal
redundancy is intentionally built into the programs that
process the files, as well as in files that contain overlapping/
redundant data. Third, a clerical review (i.e., two-person
review) of all handwritten input data is performed. A final
and extremely important means of assuring that quality
data are input into the CPS population controls is the
continuous research into improvements in methods of
making population estimates and projections.
Brooks, C.A. and Bailar, B.A. (1978), An Error Profile:
Employment as Measured by the Current Population
Survey, Statistical Policy Working Paper 3, Washington,
D.C.: Subcommittee on Nonsampling Errors, Federal Committee on Statistical Methodology, U.S. Department of
Commerce.
Kostanich, D.L. and Cahoon, L.S. (1994), Effect of
Design Differences Between the Parallel Survey and
the New CPS, CPS Bridge Team Technical Report 3, dated
March 4.
McCarthy, P.J. (1978), Some Sources of Error in
Labor Force Estimates from the Current Population
Survey, Background Paper No. 15, Washington, D.C.:
National Commission on Employment and Unemployment
Statistics.
Polivka, A.E. (1994), Comparisons of Labor Force
Estimates from the Parallel Survey and the CPS during
1993: Major Labor Force Estimates, CPS Overlap Analysis Team Technical Report 1, dated March 18.
Reeder, J.E. (1997), Regional Response to Questions
on CPS Type A Rates, Bureau of the Census, CPS Office
Memorandum No. 97-07, Methods and Performance Evaluation Memorandum No. 97-03, January 31, 1997.
15–10
Thompson, J. (1994), Mode Effects Analysis of Major
Labor Force Estimates, CPS Overlap Analysis Team
Technical Report 3, April 14,1994.
Waite, P.J. (1991), Continuing the Project Development Plan, Bureau of the Census internal memorandum,
June 7, 1991.
16–1
Chapter 16.
Quality Indicators of Nonsampling Errors
(Updated coverage ratios, nonresponse rates, and other measures of quality can be found by clicking on ‘‘Quality
Measures’’ at www.bls.census.gov/cps)
INTRODUCTION
Coverage Ratios
Chapter 15 contains a description of the different sources
of nonsampling error in the CPS and the procedures
intended to limit those errors. In the present chapter,
several important indicators of potential nonsampling error
are described. Specifically, coverage ratios, response variance, nonresponse rates, mode of interview, time in sample
biases, and proxy reporting rates are discussed. It is
important to emphasize that unlike sampling error, these
indicators show only the presence of potential nonsampling
error, not the actual degree of nonsampling error present.
Nonetheless, these indicators of nonsampling error are
regularly used to monitor and evaluate data quality. For
example, surveys with high nonresponse rates are judged
to be of low quality, but the actual nonsampling error of
concern is not the nonresponse rate itself, but rather
nonresponse bias, that is, how the respondents differ from
the nonrespondents on the variables of interest. Although it
is possible for a survey with a lower nonresponse rate to
have greater nonresponse bias than a survey that has a
higher nonresponse rate (if the nonrespondents differ to a
greater degree from the respondents in the survey with the
lower nonresponse rate), one would generally expect that
greater nonresponse indicates a greater potential for bias.
Unfortunately, while it is relatively easy to measure nonresponse rates, it is extremely difficult to measure or even
estimate nonresponse bias. Thus, these indicators are
simply a measurement of the potential presence of nonsampling errors. We are not able to quantify the effect the
nonsampling error has on the estimates, and we do not
know the combined effect of all sources of nonsampling
error.
One way to estimate the coverage error present in a
survey is to compute a coverage ratio. A coverage ratio is
a ratio of the estimated number of persons in a specific
demographic group from the survey over an independent
population total for that group. The CPS coverage ratios
are computed by dividing a CPS estimate using the weights
after the first-stage ratio adjustment by the independent
population controls used to perform the second-stage ratio
adjustment. See Chapter 9 for more information on computation of weights. Population controls are not error free.
A number of approximations or assumptions are required in
deriving them. See Appendix D for details on how the
controls are computed. Chapter 15 highlighted potential
error sources in the population controls. Undercoverage
exists when the coverage ratio is less than 1.0 and an
overcoverage when the ratio is greater than 1.0. Table 16−1
shows the average monthly coverage ratios for January
1996 to April 1996.
It is estimated that the CPS has a 92.6 percent coverage
rate for the first 4 months of 1996. This implies that the
survey is missing approximately 7.4 persons per 100. This
rate has been fairly consistent throughout the 1990s. It is
difficult to compare this coverage rate to prior decades
because of sample design changes and changes in the
method that produces the population totals, but the total
coverage rate was estimated at 96.3 percent during the
mid-1970s (Hanson, 1978).
In terms of race, Whites have the highest coverage ratio
(93.8 percent) while Blacks have the lowest (83.9 percent).
Whites and Other1 races are significantly different from
Blacks, but not significantly different from each other. Black
males age 20-29 have the lowest coverage ratio (66.2
percent) of any race/age group. Females across all races
except Other race have higher coverage ratios than males.
Hispanics2 also have relatively low coverage rates. Historically, Hispanics and Blacks have lower coverage rates than
Whites for each age group, particularly the 20-29 age
group. This is by no fault of the interviewers or the CPS
process. These lower coverage rates for minorities affect
labor force estimates because persons that are missed by
the CPS are almost surely on the average quite different
from those that are included. The persons that are missed
COVERAGE ERRORS
When conducting a sample survey, the primary goal is to
give every person in the target universe a known probability of selection into the sample. When this occurs, the
survey is said to have 100 percent coverage. This is rarely
the case, however. Errors can enter the system during
almost any phase of the survey process, from frame
creation to interviewing. A bias in the survey estimates
results when characteristics of persons erroneously included
or excluded from the survey differ from those correctly
included in the survey. Historically in the CPS, the net effect
of coverage errors has been a loss in population (resulting
from undercoverage).
1
Other includes American Indian, Eskimo, Aleut, Asian, and Pacific
Islander.
2
Hispanics may be of any race.
16–2
Table 16–1. CPS Coverage Ratios by Age, Sex, Race, and Ethnicity
[Average for January 1996–April 1996]
Age, sex, race, and
ethnicity
Average
monthly
coverage
ratio
Age, sex, race, and
ethnicity
Average
monthly
coverage
ratio
Age, sex, race, and
ethnicity
Average
monthly
coverage
ratio
Age, sex, race, and
ethnicity
Average
monthly
coverage
ratio
Total1 . . . . . . . . . . . . . . . . . .
0.9255
White . . . . . . . . . . . . . . . . . .
0.9376 Black . . . . . . . . . . . . . . . .
0.8392 Other2 race. . . . . . . . . . . .
0.9255 Hispanic3 . . . . . . . . . . . . .
0.8331
Male . . . . . . . . . . . . . . . . . . .
16-19 . . . . . . . . . . . . . . . . .
20-29 . . . . . . . . . . . . . . . . .
30-39 . . . . . . . . . . . . . . . . .
40-59 . . . . . . . . . . . . . . . . .
60-69 . . . . . . . . . . . . . . . . .
70+. . . . . . . . . . . . . . . . . . .
0.9163 Male . . . . . . . . . . . . . . . . . .
0.8974 16-19 . . . . . . . . . . . . . . . .
0.8406 20-29 . . . . . . . . . . . . . . . .
0.9091 30-39 . . . . . . . . . . . . . . . .
0.9357 40-59 . . . . . . . . . . . . . . . .
0.9705 60-69 . . . . . . . . . . . . . . . .
0.9672 70+ . . . . . . . . . . . . . . . . . .
0.7727 Male . . . . . . . . . . . . . . . . . .
0.8328 16-19 . . . . . . . . . . . . . . . .
0.6616 20-29 . . . . . . . . . . . . . . . .
0.7104 30-39 . . . . . . . . . . . . . . . .
0.8383 40-59 . . . . . . . . . . . . . . . .
0.8735 60-69 . . . . . . . . . . . . . . . .
0.8644 70+ . . . . . . . . . . . . . . . . . .
0.9119 Male . . . . . . . . . . . . . . . . . .
0.8754 16-19 . . . . . . . . . . . . . . . .
0.8615 20-29 . . . . . . . . . . . . . . . .
0.8623 30-39 . . . . . . . . . . . . . . . .
0.9616 40-59 . . . . . . . . . . . . . . . .
1.0110 60-69 . . . . . . . . . . . . . . . .
1.0236 70+ . . . . . . . . . . . . . . . . . .
0.7841
0.8693
0.7485
0.7790
0.7904
0.7899
0.7982
Female. . . . . . . . . . . . . . . . .
16-19 . . . . . . . . . . . . . . . . .
20-29 . . . . . . . . . . . . . . . . .
30-39 . . . . . . . . . . . . . . . . .
40-59 . . . . . . . . . . . . . . . . .
60-69 . . . . . . . . . . . . . . . . .
70+. . . . . . . . . . . . . . . . . . .
0.9575 Female . . . . . . . . . . . . . . . .
0.9385 16-19 . . . . . . . . . . . . . . . .
0.9130 20-29 . . . . . . . . . . . . . . . .
0.9501 30-39 . . . . . . . . . . . . . . . .
0.9613 40-59 . . . . . . . . . . . . . . . .
0.9681 60-69 . . . . . . . . . . . . . . . .
1.0173 70+ . . . . . . . . . . . . . . . . . .
0.8931 Female . . . . . . . . . . . . . . . .
0.8676 16-19 . . . . . . . . . . . . . . . .
0.8170 20-29 . . . . . . . . . . . . . . . .
0.8650 30-39 . . . . . . . . . . . . . . . .
0.9374 40-59 . . . . . . . . . . . . . . . .
0.9541 60-69 . . . . . . . . . . . . . . . .
0.9820 70+ . . . . . . . . . . . . . . . . . .
0.9380 Female . . . . . . . . . . . . . . . .
0.9071 16-19 . . . . . . . . . . . . . . . .
0.8966 20-29 . . . . . . . . . . . . . . . .
0.9023 30-39 . . . . . . . . . . . . . . . .
0.9743 40-59 . . . . . . . . . . . . . . . .
1.0185 60-69 . . . . . . . . . . . . . . . .
1.0025 70+ . . . . . . . . . . . . . . . . .
0.8820
0.8658
0.9015
0.8920
0.8766
0.8310
0.8684
1
The estimated standard errors on the Total, White, Black, Other Race, and Hispanic groups are 0.0058, 0.0064, 0.0180, 0.0322, and 0.0198,
respectively.
2
Other includes American Indian, Eskimo, Aleut, Asian, and Pacific Islander.
3
Hispanics may be of any race.
Figure 16–1. Average Yearly Noninterview and Refusal Rates for the CPS 1964-1996
16–3
are accounted for in the CPS, but they are given labor force
characteristics the same as for those that are included in
the CPS. This produces bias in CPS estimates.
NONRESPONSE
As noted in Chapter 15, there are a variety of sources of
nonresponse in the CPS, such as unit or household
nonresponse, panel nonresponse, and item nonresponse.
Unit nonresponse, referred to as Type A noninterviews,
represents households that are eligible for interview but
were not interviewed for some reason. Type A noninterviews occur because a respondent refuses to participate in
the survey, is too ill or is incapable of completing the
interview, is not available or not contacted by the interviewer, perhaps because of work schedules or vacation,
during the survey period. Because the CPS is a panel
survey, households who respond 1 month may not respond
during a following month. Thus, there is also panel nonresponse in the CPS, which can become particularly important if CPS data are used longitudinally. Finally, some
respondents who complete the CPS interview may be
unable or unwilling to answer specific questions in the CPS
resulting in some level of item nonresponse.
Type A Nonresponse
Type A noninterview rate. The Type A noninterview rate
is calculated by dividing the total number of Type A
households (refusals, temporarily absent, noncontacts, and
other noninterviews) by the total number of eligible households (which includes Type As and interviewed households).
As can be seen in Figure 16−1, the noninterview rate for
the CPS has remained relatively stable at around 4 to 5
percent for most of the past 35 years; however, there have
been some changes during this time. It is readily apparent
from Figure 16−1 that there was a major change in the CPS
nonresponse rate in January 1994, which reflects the
launching of the redesigned survey using computer assisted
survey collection procedures. This rise is discussed below.
The end of 1995 and the beginning of 1996 also show a
jump in Type A noninterview rates that were chiefly because
of disruptions in data collection because of shutdowns of
the Federal Government (see Butani, Kojetin, and Cahoon,
1996). The relative stability of the overall noninterview rate
from 1960 to 1994 masks some underlying changes that
have occurred. Specifically, the refusal portion of the
noninterview rate has shown an increase over this same
period with the bulk of the increase in refusals taking place
from the early 1960s to the mid-1970s. In the late 1970s,
there was a leveling off so that refusal rates were fairly
constant until 1994. To compensate for this increase, there
was a corresponding decrease in the rate of noncontacts
and other noninterviews.
There also appears to be seasonal variation in both the
overall noninterview rates and the refusal rates (see Figure
16−2). During the year, the noninterview and refusal rates
have tended to increase after January until they reached a
peak in March or April at the time of the income supplement. At this point, there was a steep drop in noninterview
and refusal rates that extended below the starting point in
January until they bottomed out in July or August. The rates
then increased and approached the initial level. This pattern has been evident most years in the recent past and
appears to be similar for 1994.
Effect of the transition to a redesigned survey with
computer assisted data collection on noninterview
rates. With the transition to the redesigned CPS questionnaire using computerized data collection in January 1994,
there was a noticeable increase in the Type A nonresponse
rates as can be seen in Figures 16−1 and 16−2. As part of
this transition, there were several procedural changes in
the collection of data for the CPS, and the adjustment to
these new procedures may account for this increase. For
example, the CAPI instrument now required the interviewers to go through the entire interview, while previously
some interviewers may have conducted shortened interviews with reluctant respondents, obtaining answers to
only a couple of critical questions. Another change in the
data collection procedures was an increased reliance on
using centralized telephone interviewing in CPS. Households not interviewed by the CATI centers by Wednesday
night of interview week are recycled back to the field
representatives on Thursday morning. These recycled CATI
cases can present difficulties for the field representatives
because there are only a few days left to make contact
before the end of the interviewing period. As depicted in
Figure 16-2, there has been greater variability in the
monthly Type A nonresponse rates in CPS since the
transition in January 1994. In addition, because of events
such as the Federal Government shutdowns that affected
nonresponse rates, it remains to be seen at what level the
Type A nonresponse rates will stabilize for the new CPS.
The annual overall Type A rate, the refusal rate, and
noncontact rate (which includes temporarily absent households and other noncontacts) are shown in Table 16−2 for
the period 1993-1996. Data beyond 1996 were not available at the time of this writing.
Table 16–2. Components of Type A Nonresponse
Rates, Annual Averages for 1993-1996
[Percent distribution]
Nonresponse rate
1993
1994
1995
1996
Overall Type A. . . . . . . . . . . . . .
Noncontact . . . . . . . . . . . . . . . . .
Refusal . . . . . . . . . . . . . . . . . . . .
Other . . . . . . . . . . . . . . . . . . . . . .
4.69
1.77
2.85
.13
6.19
2.30
3.54
.32
6.86
2.41
3.89
.34
6.63
2.28
4.09
.25
Panel nonresponse. Households are selected into the
CPS sample for a total of 8 months in a 4-8-4 pattern as
described in Chapter 3. Many families in these households
16–4
Figure 16–2. Monthly Noninterview and Refusal Rates for the CPS 1984-1996
Note: The break in the refusal rate indicates government furlough.
may not be in the CPS the entire 8 months because of
moving (movers are not followed, but the new household
members are interviewed). Those who live in the same
household during the entire time they are in the CPS
sample may not agree to be interviewed each month. Table
16−3 shows the percentage of households who were
interviewed 0, 1, 2, … 8 times during the 8 months that they
were eligible for interview during the period January 1994
to October 1995. These households represent seven rotation groups (see Chapter 3) that completed all of their
rotations in the sample during this period. The vast majority
of households, 82 percent, had completed interviews each
month, and only 2 percent never participated. The remaining 16 percent participated to some degree (for further
information see Harris-Kojetin and Tucker, 1997).
the effect of nonresponse on labor force classification by
using data from adjacent months and examining the monthto-month flows of persons from labor force categories to
nonresponse as well as from nonresponse to labor force
categories. Comparisons can then be made for labor force
status between households that responded both months
with households that responded 1 month, but failed to
respond in the other month. However, the labor force status
of persons in households that were nonrespondents for
both months is unknown.
Table 16–3. Percentage of Households1 by Number
of Completed Interviews During the
8 Months in the Sample
[January 1994–October 1995]
Number of completed interviews
Effect of Type A Noninterviews on Labor Force
Classification.
Although the CPS has monthly measures of Type A
nonresponse, the total effect of nonresponse on labor force
estimates produced from the CPS cannot be calculated
from CPS data alone. It is the nature of nonresponse that
we do not know what we would like to know from the
nonrespondents, and therefore, the actual degree of bias
because of nonresponse is unknown. Nonetheless, because
the CPS is a panel survey, information is often available at
some point in time from households that were nonrespondents at another point. Some assessment can be made of
0
1
2
3
4
5
6
7
8
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
Percent
2.0
.5
.5
.6
2.0
1.2
2.5
8.9
82.0
1
Includes only households in the sample all 8 months with only
interviewed and Type A nonresponse interview status for all 8 months, i.e.,
households that were out of scope (e.g., vacant) for any month in sample
were not included in these tabulations. Movers were not included in this
tabulation.
16–5
Table 16–4. Labor Force Status by Interview/Noninterview Status in Previous and Current Month
[Average January–June 19971 percent distribution]
1st month labor force status
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2nd month labor force status
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
** p < .01
Interview in 2nd
month
Nonresponse in 2nd
month
Difference
65.98
62.45
5.35
68.51
63.80
6.87
2.53**
1.35**
1.52**
Interview in 1st
month
Nonresponse in 1st
month
Difference
65.79
62.39
5.18
67.41
63.74
5.48
1.62**
1.35**
.30*
* <. 05
1
From Tucker and Harris-Kojetin (1997).
Monthly labor force data were used for each consecutive pair of months for January through June 1997, separately for households that responded for each consecutive
pair of months, and for households that responded only 1
month and were nonrespondents the other month (see
Tucker and Harris-Kojetin, 1997). The top half of Table
16−4 shows the labor force classification in the first month
for persons in households that were respondents the
second month compared to persons who were in households that were noninterviews the second month. Persons
from households that became nonrespondents had significantly higher rates of participation in the labor force,
employment, and unemployment than persons from households that were respondents both months. The bottom half
of Table 16−4 shows the labor force classification for the
second month for persons in households that were respondents in the previous month compared to persons who
were in households that were noninterviews the previous
month. The pattern of differences is similar, but the magnitude of the differences is less. Because the overall Type
A noninterview rate is quite small, the effect of the nonresponding households on the overall unemployment rate is
also relatively small. However, it is important to remember
that the labor force characteristics of the persons in households who never responded are not measured or included
here. In addition, other nonsampling errors are also likely
present, such as those due to repeated interviewing or
month-in-sample effects (described later in this chapter).
Item Nonresponse
Another component of nonresponse is item nonresponse.
Respondents may refuse or may be unable to answer
certain items, but still respond to most of the CPS questions with only a few items missing. To examine the
prevalence of item nonresponse in the CPS, counts were
made of the refusals and ‘‘don’t know’’ responses from 10
demographic items and 94 labor force items (due to skip
patterns only some of these questions were asked of each
respondent) for all interviewed cases from January to
December 1994. The average levels of item-missing data
Table 16–5. Percentage of CPS Items With Missing
Data
Item series
Percent
Demographic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Industry and occupation . . . . . . . . . . . . . . . . . . . . . . . . . .
Earnings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.54
1.46
3.76
12.44
from most of the CPS labor force and demographic items
are shown in Table 16−5. It is likely that the bias due to
imputation for the demographic and labor force items
is quite small, but there may be a concern for earnings
data.
RESPONSE VARIANCE
Estimates of the Simple Response Variance
Component
To obtain an unbiased estimate of the simple response
variance, it is necessary to have at least two independent
measurements of the characteristic for each person in a
subsample of the entire sample using the identical measurement procedure on each trial. It is also necessary that
responses to a second interview not be affected by the
response obtained in the first interview.
Two difficulties occur in every attempt to measure the
simple response variance by a reinterview. The first is lack
of independence between the original interview and the
reinterview; a person visited twice within a short period and
asked the same questions may tend to remember his/her
original responses and repeat them. A second difficulty is
that the data collection methods used in the original
interview and in the reinterview are seldom the same. Both
of these difficulties exist in the CPS reinterview program
data used to measure simple response variance. For
example, the interviews in the response variance reinterview sample are conducted by telephone by senior interviewers or supervisory personnel. Also, some characteristics of the reinterview survey process itself introduce
16–6
Table 16–6. Comparison of Responses to the Original
Interview and the Reinterview
Original interview
Reinterview
Not
unemployed
Total
a
c
a+c
b
d
b+d
a+b
c+d
n=a+b+c+d
unknown biases in the estimates (e.g., a high noninterview
rate.)
It is useful to consider a schematic representation of the
results of the original CPS interview and the reinterview in
estimating, say, the number of persons reported as unemployed (see Table 16-6). In this schematic representation,
the number of identical persons having a difference between
the original interview and the reinterview for the characteristic unemployed is given by (b + c). If these responses are
two independent measurements for identical persons using
identical measurement procedures, then an estimate of
the simple response variance component is given by
(b + c) /2n and
E
b⫹c
( )
2n
2
sum of the simple response variance, ␴R, and the popu2
Unemployed
Unemployed . . . . . . .
Not unemployed . . . .
Total . . . . . . . . . . . . . .
denominator of this ratio, the population variance, is the
2
= ␴R
and as in formula 14.3 (Hansen, Hurwitz, and Pritzker,
1964)3. We know that (b + c)/2n, using CPS reinterview
2
data, underestimates ␴R, chiefly because conditioning
sometimes takes place between the two responses for a
specific person resulting in correlated responses, so that
fewer differences (i.e., entries in the b and c cells) occur
than would be expected if the responses were in fact
independent.
Besides its effect on the total variance, the simple
response variance is potentially useful to evaluate the
precision of the survey measurement methods. It can be
used to evaluate the underlying difficulty in assigning
individuals to some category of a distribution on the basis
of responses to the survey instrument. As such, it is an aid
in determining whether a concept is sufficiently measurable
by a household survey technique and whether the resulting
survey data fulfill their intended purpose. For characteristics having ordered categories (e.g., number of hours
worked last week), the simple response variance is helpful
in determining whether the detail of the classification is too
fine. To provide a basis for this evaluation, the estimated
simple response variance is expressed, not in absolute
terms, but as a proportion of the estimated total population
variance. This ratio is called the index of inconsistency and
has a theoretical range of 0.0 to 100.0 when expressed as
a percentage (Hansen, Hurwitz, and Pritzker, 1964). The
3
The expression (b+c)/n is referred to as the gross difference rate;
thus, the simple response variance is estimated as one-half the gross
difference rate.
lation sampling variance, ␴S. When identical responses are
obtained from trial to trial, the simple response variance is
zero and the index of inconsistency has a value of zero. As
the variability in the classification of an individual over
repeated trials increases (and the measurement procedures become less reliable), the value of the index increases
until, at the limit, the responses are so variable that the
simple response variance equals the total population variance. At the limit, if a single response from N individuals is
required, equivalent information is obtained if one individual is randomly selected and interviewed N times,
independently.
Two important inferences can be made from the index of
inconsistency for a characteristic. One is to compare it to
the value of the index that could be obtained for the
characteristic by best (or preferred) set of measurement
procedures that could be devised. The second is to consider whether the precision of the measurement procedures indicated by the level of the index is still adequate to
serve the purposes for which the survey is intended. In the
CPS, the index is more commonly used for the latter
purpose. As a result, the index is used primarily to monitor
the measurement procedures over time. Substantial changes
in the indices that persist for several months result in
review of field procedures to determine and remedy the
cause.
Table 16−7 provides estimates of the index of consistency shown as percentages for selected labor force
characteristics for the period of February through July
1994.
Table 16–7. Index of Inconsistency for Selected Labor
Force Characteristics February–July 1994
Labor force characteristic
Working, full time . . . . . . . . . . . . . . . . . . . . . .
Working, part time . . . . . . . . . . . . . . . . . . . . .
With a job, not at work. . . . . . . . . . . . . . . . . .
Unemployed. . . . . . . . . . . . . . . . . . . . . . . . . . .
Not in labor force . . . . . . . . . . . . . . . . . . . . . .
Estimate of
index of
inconsistency
90 percent
confidence
limits
9.4
22.6
30.1
30.8
8.1
8.4 to 10.6
20.3 to 25.0
24.5 to 37.0
25.9 to 36.5
7.1 to 9.2
MODE OF INTERVIEW
Incidence of Telephone Interviewing
As described in Chapter 6, the first and fifth months’
interviews are typically done in person while the remaining
interviews may be done over the telephone either by the
field interviewer or an interviewer from a centralized telephone facility. Although the first and fifth interviews are
supposed to be done in person, the entire interview may
16–7
not be completed in person, and the field interviewer may
call the respondent back to obtain missing information. The
CPS CAPI instrument records whether the last contact with
the household was by telephone or personal visit. The
percentage of CAPI cases from each month-in-sample that
were completed by telephone are shown in Table 16−8.
Overall, about 66 percent of the cases are done by
telephone, with almost 85 percent of the cases in monthin-samples 2-4 and 6-8 done by telephone. Furthermore, a
substantial percentage of cases in months 1 and 5 are
obtained by telephone, despite standing instructions to the
field interviewers to conduct personal visit interviews.
Table 16–8. Percentage of Households With
Completed Interviews With Data Collected by Telephone (CAPI Cases Only)
[Percent]
Month-in-sample
1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Last contact
with household was
telephone
(average,
Jan.–Dec.
1995)
Majority of
data
collected by
telephone
(June 1996)
9.6
80.2
83.1
83.6
23.5
83.1
84.1
84.5
15
79
83
83
34
82
84
85
Because the indicator variable in the CPS instrument
reflects only the last contact with the household, it may not
be the best indicator of how most of the data were gathered
from a household. For example, an interviewer may obtain
information for several members of the household during
the first month’s personal visit, but may make a telephone
call back to obtain the labor force data for the last household member resulting in the interview being recorded as a
telephone interview. In June 1996, an additional item was
added to the CPS instrument (for that month only) that
asked interviewers whether the majority of the data for
each completed case was obtained by telephone or personal visit. The results using this indicator are presented in
the second column of Table 16-8. It was expected that in
MIS 1 and 5 interviewers would have reported that the
majority of the data was collected though personal visit
more often than was revealed by the last contact with the
household. This would seem likely because, as noted
above, the last contact with a household may be a telephone call back to obtain missing information not collected
at the initial personal visit. However, a higher percentage of
cases were reported with the majority of the data being
collected by telephone than the percentage of cases with
the last contact with the household being by telephone for
MIS 1 and 5. The explanation for this pattern of results is
not clear at the present time.
Effects of Centralized Telephone Interviewing
With the implementation of the revised CPS in January
1994, there was an increased reliance on the use of the
Census Bureau’s centralized telephone centers for conducting CPS interviews. As noted in Chapter 4, only cases
in CATI eligible PSUs can be sent to the CATI centers for
interviewing. Furthermore, all cases within eligible PSUs
were randomly assigned to the CATI panel or the control
(CAPI) panels, so that meaningful comparisons could be
made on the effects of centralized interviewing on estimates from CPS. All cases were interviewed by CAPI in
MIS 1 and 5. In months 2-4 and 6-8, most, but not all of the
cases in the CATI panel were interviewed in the centralized
facilities, because some cases were still interviewed by
field staff in CAPI for a variety of reasons. Nonetheless, to
preserve the integrity of the research design, the results
shown in Table 16−9 reflect the panel that each case was
assigned to, which was also the actual mode of interview
for the vast majority of cases
Table 16−9 shows the results of the comparisons of the
CATI test and control panels. There were significant differences found between the CATI test and control panels in
MIS 2-4 and 6-8 for all groups on the unemployment rate.
There were no significant differences in the unemployment
rate in MIS 1 and 5 (and none would be expected given
random assignment); however, the observed differences
were not necessarily equal to zero. These initial differences
may have had some slight effects on the size of the
differences observed in MIS 2-4 and 6-8 for some of the
groups. There were no significant differences for the civilian labor force participation rate or the employment to
population ratio. Although there are differences in unemployment rates between the centralized CATI and CAPI
interviews as noted above4, the interpretation of the findings is not as clear. One major difference between CATI
and CAPI interviews is that CAPI interviews are likely to be
done by the same interviewer all 8 MIS, while CATI
interviews involve a definite change in interviewers from
MIS 1 to 2 and from MIS 5 to 6. In fact, cases in the CATI
facilities often have a different interviewer each month.
However, it is not clear whether the results from the
centralized CATI or the CAPI interviews are more accurate.
TIME IN SAMPLE
The rotation pattern of the CPS sample was described in
detail in Chapter 3, and the use of composite estimation in
CPS was discussed briefly in Chapter 9. The effects of
interviewing the same respondents for CPS several times
has been discussed for a long time (e.g., Bailar, 1975;
Brooks and Bailar, 1978; Hansen, Hurwitz, Nisselson, and
Steinberg, 1955; McCarthy, 1978; Williams and Mallows,
4
These findings were also similar to those from a test conducted
using the parallel survey (see Thompson, 1994).
16–8
Table 16–9. Effect of Centralized Telephone Interviewing on Selected Labor Force Characteristics
[Average January 1996 - December 1996]
MIS 1 and 5
Employment status and sex
MIS 2-4 and 6-8
CATI test
Control
(CAPI)
Difference
CATI test
Control
(CAPI)
Difference
Standard
error
P-value
66.89
63.20
5.52
67.20
63.60
5.36
−0.31
−0.40
0.16
66.47
62.79
5.53
66.44
63.20
4.87
0.03
−0.41
0.66
1.09
1.06
0.21
.98
.70
.00
75.33
71.19
5.49
76.35
72.23
5.40
−1.02
−1.03
0.09
74.96
70.85
5.49
75.31
71.63
4.88
−0.35
−0.79
0.61
1.71
1.66
0.28
.84
.64
.03
59.50
56.20
5.55
59.19
56.05
5.30
0.31
0.14
0.25
59.04
55.75
5.57
58.68
55.83
4.85
0.36
−0.08
0.72
1.18
1.12
0.21
.76
.94
.00
67.50
64.22
4.86
67.59
64.37
4.76
−0.08
−0.15
0.11
67.16
63.94
4.79
66.89
63.97
4.37
0.28
−0.02
0.43
1.35
1.33
0.16
.84
.99
.01
76.44
72.67
4.94
77.38
73.74
4.70
−0.93
−1.07
0.24
76.14
72.52
4.76
76.31
72.98
4.37
−0.17
−0.46
0.39
1.98
1.94
0.23
.93
.81
.10
59.42
56.59
4.78
58.80
55.97
4.82
0.62
0.62
−0.05
59.07
56.22
4.83
58.43
55.88
4.36
0.64
0.34
0.47
1.35
1.29
0.20
.63
.79
.02
63.30
56.94
10.04
65.44
58.85
10.06
−2.14
−1.91
−.02
62.12
55.55
10.59
64.22
58.44
9.01
−2.10
−2.89
1.58
4.22
3.99
0.79
.62
.47
.04
67.23
60.33
10.26
70.24
61.76
12.08
−3.01
−1.43
−1.82
66.45
58.73
11.62
69.21
62.07
10.31
−2.75
−3.34
1.30
5.47
4.97
0.72
.61
.50
.07
60.55
54.57
9.87
62.06
56.81
8.46
−1.51
−2.24
1.41
59.03
53.27
9.75
60.75
55.90
7.97
−1.72
−2.63
1.79
4.75
4.51
0.83
.72
.56
.03
TOTAL POPULATION, 16 YEARS OLD
AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
MALES, 16 YEARS OLD AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
FEMALES, 16 YEARS OLD AND
OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
WHITE, 16 YEARS OLD AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
WHITE MALES, 16 YEARS OLD AND
OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
WHITE FEMALES, 16 YEARS OLD
AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
BLACKS, 16 YEARS OLD AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
BLACK MALES, 16 YEARS OLD AND
OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
BLACK FEMALES, 16 YEARS OLD
AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Employment to population ratio . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . .
1970). It is possible to measure the effect of the time spent
in the sample on labor force estimates from the CPS by
creating a month-in-sample index which shows the relationships of all the month-in-sample groups. This index is
the ratio of the estimate based on the sample units in a
particular month-in-sample group to the average estimate
from all eight month-in-sample groups combined, multiplied by 100. If an equal percentage of people with the
characteristic are present in each month-in-sample group,
then the index for each group would be 100. Table 16−10
shows indices for each group by the number of months
they have been in the sample. The indices are based on
CPS labor force data from September to December 1995.
For the percentage of the total population that is unemployed, the index of 108.62 for the first month households
indicates that the estimate from households in sample the
first month is about 1.0862 times as great as the average
overall eight month-in-sample groups; the index of
99.57 for unemployed for the second month households
16–9
indicates that it is about the same as the average overall
month-in-sample groups.
It should not be assumed that estimates from one of the
month-in-sample groups should be taken as the standard
in the sense that it would provide unbiased estimates, while
the estimates for the other seven would be biased. It is far
more likely that the expected value of each group is
biased—but to varying degrees. Total CPS estimates,
which are the combined data from all month-in-sample
groups (see Chapter 9), are subject to biases that are
functions of the biases of the individual groups. Since the
expected values vary appreciably among some of the
groups, it follows that the CPS survey conditions must be
different in one or more significant ways when applied
separately to these subgroups.
One way in which the survey conditions differ among
rotation groups is reflected in the noninterview rates. The
interviewers, being unfamiliar with households in sample
for the first time, are likely to be less successful in calling
when a responsible household member is available. Thus,
noninterview rates generally start above average with first
month families, decrease with more time in sample, go up
a little for households in sample the fifth month (after the 8
months the household was not in sample), and then
decrease again in the final months. (This pattern may also
reflect the prevalence of personal visit interviews conducted for each of the months-in-sample as noted above.)
An index of the noninterview rate constructed in a similar
manner to the month-in-sample group index can be seen in
Table 16−11. This noninterview rate index clearly shows
that the greatest proportion of noninterviews occurs for
cases that are in the sample for the first month and the
second largest proportion of noninterviews are due to
cases returning to the sample in the fifth month. These
Table 16–10. Month-In-Sample Bias Indexes (and Standard Errors) in the CPS for Selected Labor Force
Characteristics
[Average September - December 1995]
Month-in-sample
Employment status and sex
1
2
3
4
5
6
7
8
101.34
(.27)
108.62
(2.46)
100.26
(.26)
99.57
(2.59)
99.93
(.25)
98.27
(2.28)
100.32
(.25)
103.33
(2.59)
99.5
(.28)
99.19
(2.65)
99.5
(.24)
99.39
(2.42)
99.68
(.29)
98.74
(2.46)
99.48
(.30)
92.89
(2.66)
100.98
(.31)
107.65
(3.45)
100.26
(.31)
98.13
(3.53)
99.87
(.31)
97.19
(3.03)
100.34
(.26)
102.94
(3.41)
99.98
(.34)
103.60
(3.64)
99.55
(.29)
99.29
(3.22)
99.62
(.32)
95.60
(3.33)
99.40
(.34)
95.59
(3.73)
101.75
(.42)
110.09
(3.65)
100.26
(.38)
101.46
(3.44)
100.00
(.38)
99.62
(3.17)
100.29
(.41)
103.53
(3.42)
98.94
(.39)
93.80
(3.43)
99.44
(.37)
99.79
(3.61)
99.76
(.45)
102.25
(3.42)
99.56
(.47)
89.47
(3.16)
102.66
(1.05)
116.50
(5.84)
102.82
(.97)
104.13
(5.41)
100.04
(.99)
103.44
(5.29)
98.95
(.90)
106.24
(6.17)
99.19
(1.07)
94.56
(5.46)
99.03
(1.06)
85.48
(5.14)
99.11
(.90)
92.83
(4.94)
98.21
(1.04)
96.82
(6.00)
101.17
(1.45)
114.46
(9.48)
101.40
(1.34)
100.86
(8.33)
100.00
(1.16)
100.47
(7.49)
100.88
(1.17)
109.39
(8.97)
100.31
(1.29)
97.38
(8.74)
98.73
(1.28)
80.47
(7.19)
99.99
(1.29)
93.32
(6.97)
97.52
(1.52)
103.65
(8.95)
104.00
(1.40)
117.30
(8.72)
104.13
(1.31)
107.78
(7.00)
100.06
(1.40)
106.40
(7.30)
97.17
(1.25)
104.24
(7.92)
98.16
(1.45)
91.10
(6.76)
99.32
(1.38)
90.86
(6.86)
98.31
(1.27)
92.62
(7.42)
98.86
(1.30)
89.71
(7.49)
TOTAL POPULATION, 16 YEARS OLD
AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
MALES, 16 YEARS OLD AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
FEMALES, 16 YEARS OLD AND
OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
BLACK, 16 YEARS OLD AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
BLACK MALES, 16 YEARS OLD AND
OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
BLACK FEMALES, 16 YEARS OLD
AND OVER
Civilian labor force . . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . . . . . .
16–10
Table 16–11. Month-In-Sample Indexes in the CPS for
Type A Noninterview Rates January–
December 1994
Month-in-sample
1
2
3
4
5
6
7
8
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
Noninterview
rate index
134.0
93.1
89.3
90.0
117.9
95.6
92.4
88.2
Table 16–12. Percentage of CPS Labor Force Reports
Provided by Proxy Reporters
Percent
reporting
All interviews
Proxy reports. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Both self and proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49.68
0.28
MIS 1 and 5
Proxy reports. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Both self and proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50.13
0.11
MIS 2-4 and 6-8
Proxy reports. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Both self and proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49.53
0.35
noninterview changes are unlikely to be distributed proportionately among the various labor force categories (see
Table 16−4). Consequently, it is reasonable to assume that
the representation of labor force categories will differ
somewhat among the month-in-sample groups and that
these differences can affect the expected values of these
estimates, which would imply that at least some of the
month-in-sample bias can be attributed to actual differences in response probabilities among the month-in-sample
groups (Williams and Mallows, 1970). Although the individual probabilities are not known, they can be estimated
by nonresponse rates. However, other factors are also
likely to affect the month-in-sample patterns.
PROXY REPORTING
Like many household surveys, the CPS seeks information about all persons in the household whether they are
available for interview or not. CPS field representatives
accept reports from responsible adults in the household
(see Chapter 6 for a discussion of respondent rules) to
provide information about all household members. Respondents who provide labor force information about other
household members are called proxy reporters. Because
some household members may not know or be able to
provide accurate information about the labor force status
and activities of some household members, nonsampling
error may occur because of the use of proxy reporters.
The level of proxy reporting in the CPS had generally
been around 50 percent in the past and continues to be so
in the revised CPS. As can be seen in Table 16−12, the
month-in-sample has very little effect on the level of proxy
reporting. Thus, whether the interview is more likely to be a
personal visit (for MIS 1 and 5) or a telephone interview
(MIS 2-4 and 6-8) has very little effect.
Although one can make overall comparisons of the data
given by self-reporters and proxy reporters, there is an
inherent bias in the comparisons because proxy reporters
were the people more likely to be found at home when the
field representative called or visited. For this reason,
household members with self- and proxy reporters tend to
differ systematically on important labor force and demographic characteristics. In order to compare the data given
by self- and proxy reporters systematic studies must be
conducted that control assignment of proxy reporting status. Such studies have not been carried out.
SUMMARY
This chapter contains a description of several quality
indicators in the CPS, namely, coverage, noninterview
rates, telephone interview rates, and proxy reporting rates.
These rates can be used to monitor the processes of
conducting the survey, and they indicate the potential for
some nonsampling error to enter into the process. This
chapter also includes information on the potential effects of
nonresponse, centralized telephone interviewing, and the
month-in-sample groups on CPS estimates. This research
comes close to identifying the presence and effects of
nonsampling error in the CPS, but the results are far from
conclusive.
The full extent of nonsampling error in the CPS is
unknown. Because of the number and scope of changes
that have occurred in the questionnaire as well as in data
collection, it is also currently unclear how much previous
research on nonsampling error in the CPS is applicable to
the redesigned instrument with computerized data collection. Furthermore, new sources of nonsampling error may
have been introduced by the change in data collection
methodology and with changes in questions. Although
some potential sources of nonsampling error can be investigated with the currently available CPS data and reinterview data, the best source of information on nonsampling
error is often information provided by an external or outside
source or through a special study or experiment. At the
time of this writing, there is currently about 3 years’ data
available for internal analyses and comparison.
REFERENCES
Bailar, B.A. (1975), ‘‘ The Effects of Rotation Group Bias
on Estimates from Panel Surveys,’’ Journal of the American Statistical Association, 70, 23-30.
Brooks, C.A. and Bailar, B.A. (1978). Statistical Policy
Working Paper 3 An Error Profile: Employment as
Measured by the Current Population Survey, Subcommittee on Nonsampling Errors, Federal Committee on
Statistical Methodology, U.S. Department of Commerce,
Washington, DC.
16–11
Butani, S., Kojetin, B.A., and Cahoon, L. (1996), ‘‘Federal Government Shutdown: Options for Current Population Survey (CPS) Data Collection,’’ Paper presented at the
Joint Statistical Meetings of the American Statistical Association, Chicago, Illinois.
Hansen, M.H., Hurwitz, W.N., Nisselson, H., and Steinberg, J. (1955), ‘‘The Redesign of the Current Population
Survey,’’ Journal of the American Statistical Association, 50, 701-719.
Hansen, M.H., Hurwitz, W.N., and Pritzker, L. (1964),
‘‘Response Variance and Its Estimation,’’ Journal of the
American Statistical Association, 59, 1016-1041.
Hanson, R. H. (1978), The Current Population Survey: Design and Methodology, Technical Paper 40,
Washington, DC: Government Printing Office.
Harris-Kojetin, B.A. and Tucker, C. (1997), ‘‘Longitudinal
Nonresponse in the Current Population Survey,’’ Paper
presented at the 8th International Workshop on Household
Survey Nonresponse, Mannheim, Germany.
McCarthy, P.J. (1978), Some Sources of Error in
Labor Force Estimates From the Current Population
Survey, Background Paper No. 15, National Commission
on Employment and Unemployment Statistics, Washington, DC.
Thompson, J. (1994). ‘‘Mode Effects Analysis of Labor
Force Estimates,’’ CPS Overlap Analysis Team Technical Report 3, Bureau of Labor Statistics and U.S. Census
Bureau, Washington, DC.
Tucker, C. and Harris-Kojetin, B.A. (1997), ‘‘The Impact
of Nonresponse on the Unemployment Rate in the Current
Population Survey,’’ Paper presented at the 8th International Workshop on Household Survey Nonresponse,
Mannheim, Germany.
Williams, W.H. and Mallows, C.L., (1970), ‘‘Systematic
Biases in Panel Surveys Due to Differential Nonresponse,’’
Journal of the American Statistical Association, 65,
1338-1349.
A–1
Appendix A.
Maximizing Primary Sampling Unit (PSU) Overlap
INTRODUCTION
The sampling of primary sampling units (PSUs) for the
redesigned CPS was accomplished using linear programming techniques that maximized the overlap with the PSUs
from the former 1980s design while maintaining the correct
overall probabilities of selection for a probability proportional to size (pps) sampling scheme. This strategy enables
the fullest use of field staff who are already hired and
trained and results in substantial cost savings. Procedures
are already in place in the previously existing PSUs for
identifying and sampling new construction housing units. In
addition to the cost savings, a large PSU overlap prevents
overly large breaks in data series that can occur when
many PSUs are changed with the introduction of a new
design.
There were 379 nonself-representing (NSR) PSUs in the
1980 CPS design. (There were some small rotating PSUs,
but only those still in sample at the end of the 1980 design
are counted.) The maximizing procedure retained 249 (66
percent) of these NSR PSUs for the 1990 design. Had
selection for the 1990 design been independent of the
1980 design, only 149 (39 percent) would have been
retained.
Methods of solving the problem of maximizing PSU
overlap, while maintaining the correct unconditional design
probabilities, have evolved over several decades. A limited
solution was presented by Keyfitz (1951) for one PSU per
stratum pps designs similar to the CPS. The definitions of
individual PSUs were held constant, and the strata were
assumed identical. Only the PSU probabilities of selection
were allowed to be different for the former and new
designs, thus responding to relative PSU population size
changes over a decade. Raj (1968) demonstrated that the
problem solved by Keyfitz could be reformulated as a
transportation problem, which is commonly solved using
linear programming techniques. Causey, Cox, and Ernst
(1985) generalized the transportation problem approach to
allow PSU redefinitions, changes in stratum composition,
and more than one sample PSU per stratum. The generalization assumes the former design sampling to be independent within strata, which does not hold for the CPS
since the 1980s design itself had PSU overlap maximized
from the earlier 1970s design. This and other practical
considerations are addressed by various authors, including
Ernst (1986, 1990).
To illustrate the concepts, consider the following example
adapted from Causey et al. (1985). The PSUs and strata do
not change across designs, and a particular stratum has
three PSUs with probabilities p1.=.36, p2.=.24 and p3.=.4 for
the former design. Over the decade, PSUs 1 and 2 have
grown in size relative to PSU 3. Were sampling done anew
without regard for the former design, then p.1=.5, p.2=.3,
and p.3=.2 would be the probabilities for the new design. To
obtain the maximum overlap of PSUs between the former
and new designs, without restraint, we would arbitrarily
choose for the new design any PSU that had been sampled
for the former design. The implication of the arbitrary
strategy for the example would be that PSU 3, which has
been losing in relative size and is now the least populous
PSU in the stratum, would retain the highest probability of
selection (.4 from the former design). In general, the
arbitrary strategy introduces bias by favoring declining
PSUs over other PSUs, thus causing growing PSUs to be
underrepresented.
Keyfitz’s method of maximizing PSU overlap while maintaining the new design probabilities is intuitive. If PSU 1
was actually sampled for the former design, include it for
the new design since the design probability has increased
over the decade from p1.=.36 to p.1=.5. (We say the
conditional probability of choosing PSU 1 for the new
design, given that PSU 1 was sampled for the former
design, is 1.0 or certainty.) Similarly, the design probability
of PSU 2 has increased from p2.=.24 to p.2 =.3, so if it was
actually sampled for the former design then include it for
the new design. (The conditional probability of choosing
PSU 2 for the new design, given that PSU 2 was sampled
for the former design, is 1.0 or certainty.) On the other
hand, the design probability of selection for PSU 3 has
decreased from p3.=.4 to p.3=.2. If PSU 3 was the one
selected for the former design, the probability can be cut
down to size by giving it a 1/2 chance of remaining for the
new design and a 1/2 chance of being replaced. (The
conditional probability of choosing PSU 3 for the new
design, given that PSU 3 was sampled for the former
design, is .5 or 1/2.) The overall probability of PSU 3 being
in the sample for the new design is .4 x 1/2 = .2, which is
exactly the probability required for the new design.
The following tables of conditional and joint probabilities
have been partially completed. (A joint probability pij is the
probability that the ith PSU was sampled for the former
design and the jth PSU for the new design.) The question
marks (?) indicate that if PSU 3 was sampled for the former
design, but not conditionally reselected for the new design,
then one of the other PSUs needs to be selected.
A–2
Conditional probabilities
Joint probabilities pij
New PSU j
Former PSU i
i=1 p1.=.36
i=2 p2.=.24
i=3 p3.=.4
j=1
1.0
0
?
p.1=.5
j=2
0
1.0
?
p.2=.3
New PSU j
j=3
0
0
.5
p.3=.2
The joint probability table is very easy to construct. The
rows must add to the former design sampling probabilities
and the columns must add to the new design probabilities.
Also, the pij entries must all add to 1.0. The joint probabilities shown are the products of the initial former design
probabilities times the conditional probabilities (diagonal
entries: .36 x 1.0 = .36 for PSU 1; .24 x 1.0 = .24 for PSU
2; .4 x .5 = .2 for PSU 3). For the PSU 1 row, the offdiagonal joint elements are zero; that is, never choose a
Former PSU i
i=1 p1.=.36
i=2 p2.=.24
i=3 p3.=.4
j=1
.36
0
?
p.1=.5
different PSU for the new design if PSU 1 was sampled for
the former design. However, in the PSU 1 column something is missing. There is a .36 chance that PSU 1 was
sampled for the former design, then carried over to the new
design, but to give PSU 1 the desired .5 unconditional
probability for the new design, it needs an extra .14 in its
column. Similarly, PSU 2 needs an extra .06 in its column.
The completed tables follow.
Conditional probabilities
Former PSU i
i=1 p1.=.36
i=2 p2.=.24
i=3 p3.=.4
j=1
1.0
0
.35
new p.1=.5
New PSU j
j=2
0
1.0
.15
p.2=.3
Joint probabilities pij
j=3
0
0
.5
p.3=.2
The rows and columns of the joint probability table now
add properly. Any conditional probability can be derived by
dividing the corresponding joint probability by the former
design probability of the row (pij/pi.). For example, .35=.14/.4,
.15=.06/.4, and .5=.2/.4 in the PSU 3 row. Any joint
probability can be derived by multiplying the corresponding
joint probability by the former design probability of the row.
Continuing with the PSU 3 row, .14 = .35 x .4, .06 = .15 x
.4, and .2 = .5 x .4.
The diagonal sum of .8 on the joint probability table is
the unconditional probability of including the same PSU in
the sample of both the former and new designs. It is as
large as possible given that additivity to both the former
probabilities (rows) and unconditional new probabilities
must be preserved. That is, the diagonal sum has been
maximized subject to row and column constraints. A mathematical description of the problem is:
3
maximize
兺 pii
i⫽1
subject to
兺 pij ⫽ pi. i⫽1,2,3
j⫽1
3
3
and
j=2
j=3
0
0
.24
0
?
.2
p.2=.3
p.3=.2
diagonal sum = .36 +.24 +.2 = .8
兺 pij ⫽ p .j j⫽1,2,3
i⫽1
This is a special case of the maximizing/minimizing problem that is called a transportation problem. The pij of the
general transportation problem can be any nonnegative
Former PSU i
i=1 p1.=.36
i=2 p2.=.24
i=3 p3.=.4
New PSU j
j=2
j=3
0
0
.24
0
.06
.2
p.2=.3
p.3=.2
diagonal sum = .36+.24 +.2 = .8
j=1
.36
0
.14
p.1= .5
variable, and the pi. and p.j are prespecified constants.
n
m
maximize
兺 兺 cij pij
i⫽1 j⫽1
subject to
兺 pij ⫽ pi. i⫽1,..,n
j⫽1
m
n
n
and
兺 pij ⫽ p.j j⫽1,..,m
i⫽1
m
兺 p.j
兺 pi. ⫽ j⫽1
i⫽1
It is this generalized form of the transportation problem
that has been adapted for the CPS. For the 1990s design,
PSUs were redefined. In nonself-representing areas, new
1990s design strata were formed. A new stratum can be
formed from pieces of several already existing strata. Even
a simple example indicates the type of difficulties that may
arise when the stratum structure is changed. Suppose a
stratum is unchanged, except that to one PSU a county has
been added from a previously existing PSU from a different
previously existing stratum. The new design unconditional
probabilities of selection are no more difficult to calculate
than before, but several factors complicate maximizing the
overlap. Now two previously existing strata contribute to
the new stratum. For the unchanged PSUs, there is still a
1-1 matchup between the former design and the new
design, but the changed PSU is linked to two previously
existing PSUs, each of which had a chance of selection.
A–3
Note that the added county had no independent chance of
selection in the former design, but certainly contributed to
the chance of selection of its previously existing PSU. Joint
former/new design probabilities are determined that maximize the overlap between designs, taking into account
such difficulties and maintaining the proper probability
structure. A few numerical examples illustrate how this and
other simple changes in the stratum PSU structure are
dealt with.
a only
Select PSU a with conditional probability
1.0, since the former design probability
.288 is less than the new probability .5.
Enter .288 as the joint probability.
a and d
We want to select PSU a, if possible, since
its relative size (.5) is much larger than the
size of the cadd county of PSU d (.05). Since
the sum of former design probabilities (.288
+ .072) is less than the new design probability .5, PSU a can be selected with
conditional probability 1.0.
b only
Select PSU b since .192 is less than the
new probability .3.
b and d
Select PSU b since its relative size (.3) is
much larger than the cadd county size (.05),
and .192 + .048 < .3.
cold and d
Select PSU cnew. This possibility takes precedence over cold only, since there is more
overlap.
cold only
It is advantageous to select cnew, but an
extra joint probability of .12=.2–.08 is all
that can be allowed (the column must add
to .2). The corresponding conditional probability is .375= .12/.32. PSU cold comprises
three-fourths of PSU cnew, and that factor is
entered in parentheses after the joint probability. The entries in the PSU a and b
columns ensure the proper additivity of the
joint probabilities, and the (0) after the joint
probabilities indicate no overlap.
ADDING A COUNTY TO A PSU
Suppose a stratum changes between the former and
new designs only by adding a county to one of its three
PSUs. To simplify notation, let the stratum in the former
design consist of PSUs a, b, and cold with probabilities of
selection p(a)=.36, p(b)=.24, and p(cold)=.4. In the new
design, the stratum consists of PSUs a, b, and cnew with
probabilities of selection p(a)=.5, p(b)=.3, and p(cnew)=.2.
In response to a declining population, county cadd has been
added to cold to create cnew. Suppose cadd was part of
former design PSU d, and let p(d)=.2 be the former design
probability of selection for PSU d in its stratum. Also
suppose cadd accounts for one-fourth of the new design
selection size of cnew (cadd has size .05). The Former
Design Probabilities column in the table below gives all of
the former design selection possibilities and their probabilities that sum to 1: either a, b, or cold was sampled; PSU d
may or may not have been sampled. The joint probability
entries are constrained to sum to the probabilities of
selection for both the former design (rows) and the new
design PSUs (columns). The computer programs that solve
the general transportation problem ensure that the maximum sum of joint overlap probabilities is achieved (.77).
The following reasoning can be used to arrive at the
solution.
Former design probabilities
p(a only) = .36(1-.2) =.288
p(a and d)= .36(.2) =.072
p(b only) = .24(1-.2) =.192
p(b and d)= .24(.2) =.048
p(cold and d)= .4(.2) =.08
p(cold only)= .4(1-.2) =.32
Conditional probabilities
Joint probabilities
New PSU
New PSU
a
1.0
1.0
0
0
0
.4375
b
0
0
1.0
1.0
0
.1875
cnew
0
0
0
0
1.0
.375
a
.288 (1)
.072 (1)
0
0
0
.14 (0)
b
0
0
.192 (1)
.048 (1)
0
.06 (0)
cnew
0
0
0
0
.08 (1)
.12 (3/4)
p(a)= .5
p(b)=.3
p(cnew)=.2
p(a)= .5
p(b)=.3
p(cnew)=.2
overlap sum = .288 +.072 + .192 + .048 + .08 + .12 (3/4) = .77
DROPPING A COUNTY FROM A PSU
Suppose a stratum changes between the former and
new designs only by dropping a county from one of its three
PSUs. Let the stratum in the former design consist of PSUs
a, b, and cold with probabilities of selection p(a)=.36,
p(b)=.24, and p(cold)=.4. In the new design, the stratum
consists of PSUs a, b, and cnew with probabilities of
selection p(a)=.5, p(b)=.3, and p(cnew)=.2. County cdrop has
been dropped from cold to create cnew. Note that dropping
A–4
a county explains part of the former-to-new design decrease
in probability for PSU c, but the solution for maximum
overlap is really the same as for the first example in this
appendix (see the table below). The (1) after the .2 joint
Former design probabilities
probability entry means that (1) if cold was sampled for the
stratum in the former design and (2) cnew is selected for the
stratum in the new design, then(3) all of cnew overlaps with
the former design.
Conditional probabilities
Joint probabilities
New PSU
a
1.0
0
.35
p(a)=.5
p(a)= .36
p(b)= .24
p(cold)= .4
b
0
1.0
.15
p(b)=.3
New PSU
cnew
0
0
.5
p(cnew)=.2
a
.36 (1)
0
.14 (0)
p(a)=.5
b
0
.24 (1)
.06 (0)
p(b)=.3
cnew
0
0
.2 (1)
p(cnew)=.2
Overlap sum = .36 +.24 + .2 = .8
DROPPING AND ADDING PSU’S
b only
Suppose one PSU is dropped from a stratum and
another added. Let the stratum in the former design consist
of PSUs a, b, and c with probabilities of selection p(a)=.36,
p(b)=.24, and p(c)=.4. In the new design, the stratum
consists of PSUs b, c, and d (drop a; add d) with probabilities of selection p(b)=.3, p(c)=.2, and p(d)=.5. In the former
design p(d)=.5 in its stratum. The Former Design Probabilities column in the table below gives all of the former design
selection possibilities and their probabilities that sum to 1:
either a, b or c was sampled in the previously existing
stratum; PSU d may or may not have been sampled. The
joint probability entries are constrained to sum to the
probabilities of selection for both the former design (rows)
and the new design PSUs (columns). The computer programs that solve the general transportation problem ensure
that the maximum sum of joint overlap probabilities is
achieved (.82). The following reasoning can be used to
arrive at the solution.
d only
c only
d and c
d and b
none
Select PSU d with conditional probability
1.0, since the former design probability .18
Former design probabilities
p(d only) = .5(1-.24-.4) =.18
p(b only)= .24(1-.5) =.12
p(c only) = .4(1-.5) =.2
p(d and c)= .5(.4) =.2
p(d and b)= .5(.24) =.12
p(none)= (1-.5)(1-.24-.4) =.18
is less than the new probability .5.
Select PSU b with conditional probability
1.0, since the former design probability .12
is less than the new probability .3.
Select PSU c with conditional probability
1.0, since the former design probability .2
does not exceed the new probability .2.
Select PSU d with conditional probability
1.0. PSU c cannot be specified since its
joint probability column already adds to the
new probability .2.
Select PSU d with conditional probability
1.0. PSU b could have been specified, but
selecting PSU d provides more overlap with
the former design.
It is possible (probability .18) that none of
the new stratum PSUs were sampled for
the former design. Selecting PSU b with
conditional probability 1.0 ensures that all
the joint probability columns sum to the new
design PSU probabilities. The (0) after the
joint probability indicates that there is no
overlap.
Conditional probabilities
Joint probabilities
New PSU
New PSU
d
1.0
0
0
1.0
1.0
0
p(d)=.5
b
0
1.0
0
0
0
1.0
p(b)=.3
c
0
0
1.0
0
0
0
p(c)=.2
d
.18 (1)
0
0
.2 (1)
.12 (1)
0
p(d)=.5
b
0
.12 (1)
0
0
0
.18 (0)
p(b)=.3
c
0
0
.2(1)
0
0
0
p(c)=.2
overlap sum = .18 +.12 + .2 + .2 + .12 = .82
A–5
REFERENCES
Causey, B. D., Cox, L.H., and Ernst, L.K. (1985), ‘‘Applications of Transportation Theory to Statistical Problems,’’
Journal of the American Statistical Association, 80,
903-909.
Ernst, L. (1986), ‘‘Maximizing the Overlap Between
Surveys When Information Is Incomplete,’’ European Journal of Operations Research, 27, 192-200.
Ernst, L. (1990), ‘‘Formulas for Approximating Overlap
Probability When Maximizing Overlap,’’ U.S. Census Bureau,
Statistical Research Division, Technical Note Series, SRD
Research Report Number Census/SRD/TN-90/01.
Gunlicks, C. (1992), ‘‘PSU Stratification and Selection
Documentation: Comparison of the Overlap Program and
the Expected Number of 1980 PSUs Selected Without the
Overlap Program in the 1990 Redesign PSU Selection,’’
(S-S90 DC-6), Memorandum to documentation, July 14,
1992.
Keyfitz, N. (1951), ‘‘Sampling With Probabilities Proportional to Size: Adjustment for Changes in Probabilities,’’
Journal of the American Statistical Association, 46,
105-109.
Perkins, W. M. (1970), ‘‘1970 CPS Redesign: Proposed
Method for Deriving Sample PSU Selection Probabilities
Within 1970 NSR Strata,’’ Memorandum to J. Waksberg,
U.S. Census Bureau, August 5, 1970.
Perkins, W. M. (1971), ‘‘1970 CPS Redesign: Illustration
of Computation of PSU Probabilities Within a Given 1970
Stratum,’’ Memorandum to J. Waksberg, U.S. Census
Bureau, February 19, 1971.
Raj, D. (1968), Sampling Theory, New York: McGraw
Hill.
Roebuck, M. (1992), ‘‘1990 Redesign: PSU Selection
Specifications’’ (Document #2.4-R-7), Memorandum to B.
Walter through G. Shapiro and L. Cahoon, September 25,
1992.
B–1
Appendix B.
Sample Preparation Materials
INTRODUCTION
Despite the conversion of CPS to an automated interviewing environment, a number of paper materials are still
in use. These materials include segment folders, listing
sheets, maps, etc. Regional office staff and field representatives use these materials as aids in identifying and
locating selected sample housing units. This appendix
provides illustrations and explanations of these materials
by frame (unit, area, group quarters, and permit). The
information provided here should be used in conjunction
with the information in Chapter 4 of this document.
UNIT FRAME MATERIALS
Segment Folders, (BC-1669 (CPS)) Illustration 1
A field representative receives a segment folder for each
segment assigned for listing, updating, or interviewing.
Segment folders were designed to:
1. Hold all materials needed to complete an assignment.
2. Provide instructions on listing, updating, and sampling.
The segment folder cover provides the following information about a segment:
1. Identifying information about the segment such as the
regional office code, the field primary sampling unit
(PSU), place name, sample designations for the segment, and basic geography.
2. Instructions for listing and updating. Part I of the
segment folder contains ‘‘Field Representative Listing
and Updating Instructions.’’ This information is provided by the regional office and varies by segment
type.
3. Sampling instructions. Part II on the segment folder is
used only for area and group quarters segments. The
regional offices use this section, ‘‘Regional Office
Sampling Instructions,’’ to record Start-with/Take-everys
as described in Chapter 4.
4. Instructions for precanvassing and determining year
built in area segments. Part III on the segment folder is
reserved for stamps that provide the field representatives with information on whether or not to precanvass
and whether or not to determine the year a structure
was built.
5. Helpful information about the segment entered by the
regional office or the field representative in the Remarks
Section.
A segment folder for a unit segment may contain some
or all of the following: Unit/Permit Listing Sheets (Form
11-3), Incomplete Address Locator Actions (Form BC-1718
(ADP)), 1990 Combined Reference File (CRF) listings, and
1990 Census Maps (county or census spotted).
A segment folder for an area segment may contain some
or all of the following: Area Segment Listing Sheets (Form
11-5), Group Quarters Listing Sheets (Form 11-1), Area
Segment Maps, Segment Locator Maps, County Maps,
and Map Legends.
A segment folder for a group quarters segment may
contain some or all of the following: Group Quarters Listing
Sheets (Form 11-1), Incomplete Address Locator Actions
(Form BC-1718 (ADP)), 1990 Census Maps (county or
census spotted), and 1990 Combined Reference File listings.
A segment folder for a permit segment may contain
some or all of the following: Unit/Permit Listing Sheets
(Form 11-3), photocopies of the listed Permit Address List
(PAL) (Form 11-193A), and Permit Sketch Maps (Form
11-187).
Unit/Permit Listing Sheets (Form 11-3)
Illustrations 2–4
Unit/Permit Listing Sheets are provided for all multiunit
addresses in unit segments. For each sample housing unit
at a multiunit address, the field representative receives a
preprinted, computer generated listing. This listing aids the
field representative in locating the sample units and, in
most cases, will eliminate the need to relist the entire
multiunit address.
Listings for multiunit addresses are sorted into two
categories: small and large. The type of multiunit listing is
identified below the listing sheet title. The differences
between these listings are identified in Table B–1.
Illustration 1. Segment Folder, BC-1669(CPS)
B–2
B–3
Table B–1. Listings for Multiunit Addresses
Listing type
Expected number of units
Listing characteristics
Illustration number
Small
2-9
Lists all expected units and could have some
2
units with missing and/or duplicate unit designations as recorded in the 1990 census
Large
10 or more
Lists sample units only. On rare occasions, lists
3
both sample and nonsample units
4
Large (Special Exception)
10 or more
No unit designations are listed and the Footnotes
Section instructs the field representative to ‘‘List
all units at the basic address.’’
The field representative also receives blank Unit/Permit
Listing Sheets for use when an address needs relisting or
when the field representative finds more units than expected
causing him/her to run out of lines on the preprinted listing
sheet.
Single-unit addresses in unit segments are not listed on
Unit/Permit Listing Sheets.
Incomplete Address Locator Actions
(Form BC-1718(ADP)) Illustration 5
The Incomplete Locator Actions form is used when the
information obtained from the 1990 census is in some way
incomplete (i.e., missing a house number, unit designation,
etc.). The form provides the field representative with additional information that can be used to locate the incomplete
address. The information on the Incomplete Address Locator Actions includes:
1. The address as it appeared in the Combined Reference File and possibly a complete address resulting
from research conducted by Census Bureau staff;
2. A list of additional materials provided to the field
representative to aid in locating the address; and
3. An outline of the actions the field representative should
take to locate the address.
After locating the address, the field representative completes or corrects the basic address on the Incomplete
Address Locator Actions form.
1990 Combined Reference File Listings
For each assigned segment that has at least one
incomplete basic address, the field representative receives
a computer-generated printout of addresses from the 1990
Combined Reference File. Each printout contains one or
more incomplete addresses along with complete addresses
in the same block.
The field representative uses the Combined Reference
File listing by finding the incomplete basic address on the
listing. Then the field representative uses the addresses
listed directly before and after it to physically locate the
incomplete address.
1990 Census Maps (County or Census Spotted)
For each assigned segment that has at least one
incomplete basic address, the field representative receives
a 1990 county or census-spotted map. For each incomplete basic address, the following control numbers are
entered in red at the top of the map: field PSU number,
sample designation, segment number, and serial number.
The map spot of the incomplete address will be circled in
red. The field representative uses the surrounding map
spots to locate the incomplete address.
AREA FRAME MATERIALS
Segment Folder (BC-1669(CPS)) Illustration 1
See the description and illustration in the Unit Frame
Materials above.
Area Segment Listing Sheets (Form 11-5)
Illustration 6
Area Segment Listing Sheets are used to record information about housing units within the assigned block and
to record information on the year a structure was built
(when necessary). A field representative receives several
Area Segment Listing Sheets for a block since the actual
number of housing units in that block is unknown until the
listing is complete. Only one of the listing sheets has the
heading items filled in by the regional office. The heading
items include: regional office code, field PSU, segment
number, and survey acronym.
Group Quarters Listing Sheets (Form 11-1)
Illustration 10
See the description and illustration provided in the
Group Quarters Frame Materials below.
Area Segment Map Illustration 7
An Area Segment Map is a large scale map of the area
segment, but it does not show roads or features outside of
the segment boundaries. Within the segment, the map
B–4
Illustration 2. Unit/Permit Listing Sheet (Unit Segment)
B–5
Illustration 3. Unit/Permit Listing Sheet (Multiunit Structure)
B–6
Illustration 4. Unit/Permit Listing Sheet (Large Special Exception)
B–7
Illustration 5. Incomplete Address Locator Actions, BC-1718 (ADP)
B–8
Illustration 6. Area Segment Listing Sheet
B–9
includes such features as highways, streets, roads, trails,
walkways, railroads, bodies of water, military installations,
and parks. The area segment map is used for:
1. Determining the exact location and boundaries of the
area segment, and
2. Mapspotting the locations of housing units when listing
the area segment.
Segment Locator Map Illustration 8
Segment Locator Maps are used to aid in the location of
the area segment. The map identifies street patterns and
names surrounding the exterior of the area segment. The
location of the area segment is a shaded area, centered on
the segment locator map.
In area segments, field representatives receive blank
group quarters listing sheets since at the time of the initial
listing, it is not known whether or not the block contains any
group quarters.
Incomplete Address Locator Actions (Form
BC-1718(ADP)) Illustration 3
See the description and illustration in the Unit Frame
Materials above.
1990 Census Maps (County or Census Spotted)
See the description in the Unit Frame Materials above.
1990 Combined Reference File Listings
County Map
Field representatives receive county maps for each field
PSU in which they work. The map is divided into blocks or
grids. Each segment locator map is one or more blocks or
grids from the county map.
Map Legend Illustration 9
Map Legends are used to help in the identification of
boundaries and features from the symbols and names
printed on the area segment map and the segment locator
map.
GROUP QUARTERS FRAME MATERIALS
Segment Folder (BC-1669(CPS)) Illustration 1
See the description and illustration in the Unit Frame
Materials above.
Group Quarters Listing Sheets (Form 11-1)
Illustration 10
The Group Quarters Listing Sheet is used to record the
name, type and address of the group quarters, the name
and telephone number of the contact person at the group
quarters and to list the eligible units within the group
quarters.
In group quarters segments, the field representative
receives group quarters listing sheets with certain information preprinted by the computer. This information includes:
group quarters name and address, group quarters type,
group quarters ID number (from the 1990 census), regional
office code, field PSU, segment number, etc.
See the description in the Unit Frame Materials above.
PERMIT FRAME MATERIALS
Segment Folder (BC-1699(CPS)) Illustration 1
See the description and illustration in the Unit Frame
Materials above.
Unit/Permit Listing Sheets (Form 11-3)
Illustration 11
For each permit address in sample for the segment, the
field representative receives a Unit/Permit Listing Sheet
with heading information, sample designations, and serial
numbers preprinted on it. The field representative also
receives blank Unit/Permit Listing Sheets in case there are
not enough lines on the preprinted listing sheet(s) to list all
units at a multiunit address.
Permit Address List (Form 11-193A)
Illustration 12
The permit address list (PAL) form is computer generated for each of the Building Permit Offices in sample. It is
used to obtain necessary address information for individual
permits issued. The PALs for Survey of Construction (SOC)
building permit offices and non-SOC building permit offices
are identical with one exception: For SOC building permit
offices, the sample permit numbers, the date issued, and
the number of housing units for each sample permit are
preprinted on the PALs. The field representatives obtain
the address information for these sample permits only. For
Illustration 7. Area Segment Map
B–10
B–11
Illustration 8. Segment Locator Map
B–12
Illustration 9. Map Legend
Current Population Survey
B–13
Illustration 10. Group Quarters Listing Sheet
B–14
Illustration 11. Unit/Permit Listing Sheet
Illustration 12. Permit Address List
B–15
B–16
Illustration 13. Permit Sketch Map
B–17
non-SOC building permit offices, the PALs do not contain
any preprinted permit numbers. Field representatives list
all residential permits issued by the building permit office
for a specific month.
Permit Sketch Map (Form 11-187) Illustration 13
When completing a PAL form, if the address given on a
building permit is incomplete, the field representative attempts
to obtain a complete address. If a complete address cannot
be obtained, the field representative visits the new construction site and draws a Permit Sketch Map. The map
shows the location of the construction site, streets in the
vicinity of the site, and the directions and distance from the
construction site to the nearest town.
C–1
Appendix C.
Maintaining the Desired Sample Size
INTRODUCTION
Implementing the Reduction Plan
The Current Population Survey (CPS) sample is continually updated to include housing units built after the most
recent census. If the same sampling rates were used
throughout the decade, the growth of the U.S. housing
inventory would lead to increases in the CPS sample size
and, consequently, to increases in cost. To avoid exceeding
the budget, the sampling rate is periodically reduced to
maintain the desired sample size. Referred to as maintenance reductions, these changes in the sampling rate are
implemented in a way that retains the desired set of
reliability requirements.
These maintenance reductions are different from changes
to the base CPS sample size resulting from modifications
in the CPS funding levels. The methodology for designing
and implementing this type of sample size change is
generally dictated by new requirements specified by the
Bureau of Labor Statistics (BLS). For example, the sample
reduction implemented in January 1996 was due to a
reduction in CPS funding and new design requirements
were specified (see Appendix H). As a result of the January
1996 reduction, however, any impending maintenance
reduction to the 1990 design sample because of sample
growth was preempted.
Reduction groups. The CPS sample size is reduced by
deleting one or more subsamples of ultimate sampling
units (USUs) from both the old construction and permit
frames. The original sample of USUs is partitioned into 101
subsamples called reduction groups; each is representative of the overall sample. The decision to use 101 subsamples is somewhat arbitrary. A useful attribute of the
number used is it is prime to the number of rotation groups
(eight) so that reductions have a uniform effect across
rotations. A number larger than 101 would allow greater
flexibility in pinpointing proportions of sample to reduce.
However, a large number of reduction groups can lead to
imbalances in sample cut distribution across PSUs since
small PSUs may not have enough sample to have all
reduction groups represented. That is, when there are a
large number of reduction groups randomly assigned, a
smaller PSU may or may not experience reductions and
quite possibly it may undergo too much reduction in the
PSU.
All USUs in a hit string have the same reduction group
number (see Chapter 3). For the unit, area, and group
quarters (GQ) frames, hit strings are sorted and then
sequentially assigned a reduction group code from 1
through 101. The sort sequence is:
MAINTENANCE REDUCTIONS
Developing the Reduction Plan
The CPS sample size for the United States is projected
forward for about a year using linear regression based on
previous CPS monthly sample sizes. The future CPS
sample size must be predicted because CPS maintenance
reductions are gradually introduced over 16 months and
operational lead time is needed to prevent dropped cases
from being interviewed.
The growth is examined in all states and major substate
areas to determine whether it is uniform or not. The states
with faster growth are candidates for a possible maintenance reduction. The remaining sample must be sufficient
to maintain the individual state and national reliability
requirements. Generally, the sample in a state is reduced
by the same proportion in all frames in all primary sampling
units (PSUs) to maintain the self-weighting nature of the
state design.
1. State or substate.
2. Metropolitan statistical area/nonmetropolitan statistical area status.
3. Self-representing/nonself-representing status.
4. Stratification PSU.
5. Final hit number, which defines the original order of
selection.
For the permit frame, a random start is generated for each
stratification PSU and permit frame hits are assigned a
reduction group code from 1 through 101 following a
specific, nonsequential pattern. This method of assigning
reduction group code is used to improve balancing of
reduction groups because of small permit sample sizes in
some PSUs and the uncertainty of which PSUs will actually
have permit samples over the life of the design. The sort
sequence is:
1. Stratification PSU.
2. Final hit number.
C– 2
The state or national sample can be reduced by deleting
USUs from both the old construction and permit frames in
one or more reduction groups. If there are k reduction
groups in sample, the sample may be reduced by 1/k by
deleting one of k reduction groups. For the first reduction
applied to redesigned samples, each reduction group
represents roughly 1 percent of the sample. Reduction
group numbers are chosen for deletion in a specific sequence
designed to maintain the nature of the systematic sample
to the extent possible.
For example, suppose a state has an overall state
sampling interval of 500 at the start of the 1990 design.
Suppose the original selection probability of 1 in 500 is
modified by deleting 5 of 101 reduction groups. The
resulting overall state sampling interval (SI) is
101
⫽ 526.0417
101 ⫺ 5
This makes the resulting overall selection probability in the
state 1 in 526.0417. In the subsequent maintenance reduction, the state has 96 reduction groups remaining. A
reduction of 1 in 96 can be accomplished by deleting 1 of
the remaining 96 reduction groups.
The resulting overall state sampling interval is the new
basic weight for the remaining uncut sample.
SI ⫽ 500 x
Introducing the reduction. A maintenance reduction is
implemented only when a new sample designation is
introduced and is gradually phased in with each incoming
rotation group to minimize the effect on survey estimates
and reliability and to prevent sudden changes to interviewer workloads. The basic weight applied to each incoming rotation group reflects the reduction. Once this basic
weight is assigned, it does not change until future sample
changes are made. In all, it takes 16 months for a maintenance sample reduction and new basic weights to be fully
reflected in all eight rotation groups interviewed for a
particular month. During the phase-in period, rotation groups
have different basic weights; consequently, the average
weight over all eight rotation groups changes each month.
After the phase-in period, all eight rotation groups have the
same basic weight.
Maintenance Reductions and GVFs
National generalized variance parameters are not updated
for maintenance reductions because the purpose of these
reductions is to maintain the sample size. However, the
national generalized parameters should be updated to
reflect any changes in sample size. For example, the
parameters were updated for the January 1996 reduction
since survey design parameters changed. (See Chapter 14
for more details.)
D–1
Appendix D.
Derivation of Independent Population Controls
INTRODUCTION
Each month, for the purpose of the iterative, secondstage weighting of the Current Population Survey (CPS)
data, independent projections of the eligible population are
produced by the Census Bureau’s Population Division.
While the CPS provides the fundamental reason for the
existence of these monthly projections, other survey-based
programs sponsored by the Bureau of the Census, the
Bureau of Labor Statistics, the National Center for Health
Statistics, and other agencies use these independent projections to calibrate surveys. These projections consist of
the adjusted civilian noninstitutional population of the United
States, distributed by demographic characteristics in three
ways: (1) age, sex, and race, (2) age, sex, and Hispanic1
origin, and (3) state of residence, restricted to the population 16 years of age and over. They are produced in
association with the Population Division’s population estimates and projections programs, which provide published
estimates and long-term projections of the population of
the United States, the 50 states and District of Columbia,
the counties, and subcounty jurisdictions.
Organization of This Appendix
The CPS population controls, like other population estimates and projections produced by the Census Bureau,
are based on a demographic framework of population
accounting. Under this framework, time series of population estimates and projections are anchored by decennial
census enumerations, with populations for dates between
two previous censuses, since the last census, or in the
future derived by the estimation or projection of population
change. The method by which population change is estimated depends on the defining demographic and geographic characteristics of the population. The issue of
adjustment for net underenumeration in the census is
handled outside of this demographic framework, and is
applied to the CPS control series as a final step in its
production.
This appendix seeks first to systematically present this
framework, defining terminology and concepts, then to
describe data sources and their application within the
framework. The first subsection is operational and is devoted
to the organization of the chapter and a glossary of terms.
1
Hispanics may be of any race.
The second and third subsections deal with two broad
conceptual definitions. The second subsection distinguishes
population estimates from population projections and describes
how monthly population controls for surveys ‘‘fit’’ into this
distinction. The third subsection defines the ‘‘CPS control
universe,’’ the set of inclusion and classification rules
specifying the population to be projected for the purpose of
weighting CPS data. The fourth subsection, ‘‘Calculation of
Population Projections for the CPS Control Universe,’’
comprises the bulk of the appendix. It provides the mathematical framework and data sources for updating total
population from a census date via the demographic components of change and how each of the requisite inputs to
the mathematical framework is measured. This is presented separately for the total population of the United
States, its distribution by demographic characteristics, and
its distribution by state of residence. The fifth subsection,
‘‘Adjustment of CPS Controls for Net Underenumeration in
the 1990 Census,’’ describes the application of data from
the 1990 Post-Enumeration Survey (PES) to include an
estimate of net underenumeration in the 1990 census. The
final two sections are, once again, operational. A subsection, ‘‘The Monthly and Annual Revision Process for Independent Population Controls’’ describes the protocols for
incorporating new information in the series through revision. We conclude with a brief section, ‘‘Procedural Revisions,’’ containing an overview of various technical problems for which solutions are being sought.
Terminology Used in This Appendix
The following is an alphabetical list of terms used in this
appendix, essential to understanding the derivation of
census-based population controls for surveys, but not
necessarily prevalent in the literature on survey methodology.
An adjusted (as opposed to unadjusted) population
estimate or projection includes an allowance for net undercoverage (underenumeration) in a census. In this appendix
(as in other Census Bureau publications), the term, when
applied to series based on the 1990 census, refers to
incorporation of the results of the 1990 Post-Enumeration
Survey (PES).
The population base, (or base population) for a population estimate or projection is the population count or
estimate to which some measurement or assumption of
population change is added to yield the population estimate or projection.
D–2
A census-level population estimate or projection is one
that can be linked to a census count through an estimate or
projection of population change; specifically, it does not
include an adjustment for underenumeration in the census.
The civilian population is the portion of the resident
population not in the active-duty military. Active-duty military, used in this context, refers to persons defined to be in
active duty by one of the five branches of the Armed
Forces, as well as persons in the National Guard or the
reserves actively participating in certain training programs.
Components of population change are any subdivisions
of the numerical population change over a time interval.
The demographic components often cited in the literature
consist of births, deaths, and net migration. In this appendix, reference is also made to changes in the institutional
and active-duty Armed Forces populations, which affect the
CPS control universe.
The CPS control universe is characterized by three
attributes: (1) restriction to the civilian noninstitutional
population, (2) modification of census data by age, race,
and sex (MARS), and (3) adjustment for net census
underenumeration. Each of the three defining concepts
appears separately in this glossary.
The Demographic Analysis Population (‘‘DA’’ population)
is a distribution of population by characteristics—in the
present case age, sex, and race—developed by observation of births, deaths by age, and international migration by
age, over a period of time for each sex and race category.
It is derived independent of census data. Thus, the number
of persons x years of age of a given sex and race is
determined by the number of births x years before the
reference date, as well as the number of deaths and net
migration of persons born the same year during the x-year
interval from birth date to reference date. In this text, we
also refer to the ‘‘DA/MARS population.’’ This is a hybrid
distribution based on demographic analysis for age, sex,
and three race groups, but on the census for Hispanic
origin detail within these groups. ‘‘MARS’’ refers to the fact
that the census detail is based on modified age and race,
as defined elsewhere in this glossary.
Emigration is the permanent departure of a person from
a country of residence, in this case the United States. In the
present context, it refers to the departure of persons legally
resident in the United States, but not confined to legally
permanent residents (immigrants). Departures of undocumented residents are excluded, because they are included
in the definition of net undocumented migration (elsewhere
in this glossary).
Population estimates are population figures that do not
arise directly from a census or count, but can be determined from available data (e.g., administrative); hence,
they are not based on assumptions or modeling. Population estimates discussed in this appendix stipulate an
enumerated base population, coupled with estimates of
population change from the date of the base population to
the date of the estimate.
Immigration is the acquisition, by a foreign national, of
legal permanent resident status in the United States.
Hence, an immigrant may or may not reside in the United
States prior to immigration. This definition is consistent with
the parlance of the Immigration and Naturalization Service
(INS), but differs from popular usage, which associates
immigration with an actual change of residence.
The institutional population refers to a population universe consisting of inmates of CPS-defined institutions,
such as prisons, nursing homes, juvenile detention facilities, or residential mental hospitals.
Internal consistency of a population time series occurs
when the difference between any two population estimates
in the series can be construed as population change
between the reference dates of the two populations estimates.
International migration is generally the change of a
person’s residence from one country to another. In the
present context, this concept includes change of residence
into the United States by non-U.S. citizens and by previous
residents of Puerto Rico or the U.S. outlying areas, as well
as the change of residence out of the United States by
persons intending to live permanently abroad, in Puerto
Rico, or the outlying areas.
Legal permanent residents are persons whose right to
reside in the United States is legally defined either by
immigration or by U.S. citizenship. In the present context,
the term generally refers to noncitizen immigrants.
Modified age, race, and sex, abbreviated MARS, describes
the census population after the definition of age and race
has been aligned with other administrative sources (or with
OMB Directive 15, in the case of race).
The natural increase of a population over a time interval
is the number of births minus the number of deaths during
the interval.
The Post-Enumeration Survey is a survey of households
conducted after the 1990 population census in a sample of
blocks to estimate the net underenumeration in the 1990
census.
Population projections are population figures relying on
modeled or assumed values for some or all of their
components, therefore not entirely calculated from actual
data. Projections discussed in this appendix stipulate a
base population that may be an estimate or count, and
components of population change from the reference date
of the base population to the reference date of the projection.
The reference date of an estimate or projection is the
date to which the population figure applies. The CPS
control reference date, in particular, is the first day of the
month in which the CPS data are collected.
The resident population of the United States is the
population usually resident in the 50 states and District of
Columbia. For the census date, this population matches
the total population in census decennial publications, although
D–3
applications to the CPS series beginning in 1995 include
some count resolution corrections to the 1990 census not
included in original census publications.
THE POPULATION UNIVERSE FOR CPS
CONTROLS
Undocumented migration (more precisely, net undocumented migration) is the net increase of population brought
about by the residential migration of persons in and out of
the country who have no legal basis for residence in the
United States.
In the concept of ‘‘population universe,’’ as used in this
appendix, are not only the rules specifying what persons
are included in the population under consideration, but also
the rules specifying their geographic locations and their
relevant characteristics, such as age, sex, race, and Hispanic origin. In this section we consider three population
universes; the resident population universe defined by the
1990 census, the resident population universe used for
official population estimates, and the CPS control universe
that relates directly to the calculation of CPS population
controls. These three universes are distinct from one
another; each one is derived from the one that precedes it;
hence, the necessity of considering all three.
The primacy of the decennial census in the definition of
these three universes is a consequence of its importance
within the Federal statistical system. This, in turn, is a result
of the extensive detail that it provides on the geographic
distribution and characteristics of the U.S. population, as
well as the legitimacy accorded it by the U.S. Constitution.
Later in this appendix, we cite another population universe—the
Demographic Analysis population—that may be technically
more appropriate as a base for estimates and projections
than the 1990 census in some regards, mainly in the
continuity of the age distribution and the conformity of
racial categories with other administrative data sources.
Because the census defines the universe for estimates, the
use of the DA population in the process is intended to
ensure the continuity of census-related biases over time in
the estimates, not to eliminate them.
A population universe is a set of rules or criteria defining
inclusion and exclusion of persons from a population, as
well as rules of classification for specified geographic or
demographic characteristics within the population. Examples
include the resident population universe and the civilian
population universe, both of which are defined elsewhere
in this glossary. Frequent reference is made to the ‘‘CPS
control universe,’’ defined separately in this glossary.
CPS Population Controls: Estimates or
Projections?
Throughout this appendix, the independent population
controls for the CPS will be cited as population projections.
Throughout much of the scientific literature on population,
demographers take care to distinguish the concepts of
‘‘estimate’’ and ‘‘projection’’; yet, the CPS population controls lie perilously close to a somewhat ragged line of
distinction. Generally, population estimates relate to past
dates, and in order to be called ‘‘estimates,’’ must be
supported by a reasonably complete data series. If the
estimating procedure involves the computation of population change from a base date to a reference date, the
inputs to the computation must have a solid basis in data.
Projections, on the other hand, allow the replacement of
unavailable data with assumptions.
The reference date for CPS population controls is
indeed in the past, relative to their date of production.
However, the past is so recent—3 to 4 weeks prior to
production—that for all intents and purposes, CPS controls
are projections. No data relating to population change are
available for the month prior to the reference date; very
little data are available for 3 to 4 months prior to the
reference date; no data for state-level geography are
available past July 1 of the year prior to the current year.
Hence, we refer to CPS controls as projections, despite
their frequent description as estimates in the literature.
Be this as it may, CPS controls are founded primarily on
a population census and on administrative data. Analysis of
coverage rates has shown that even without adjustment for
underenumeration in the controls, the lowest coverage
rates of the CPS often coincide with those age-sex-raceHispanic segments of the population having the lowest
coverage in the census. Therefore, the contribution of
independent population controls to the weighting of CPS
data is highly productive. This holds, despite the use of
modeling for missing data.
The universe defined by the 1990 census is the U.S.
resident population, including persons resident in the 50
states and the District of Columbia. This definition excludes
residents of the Commonwealth of Puerto Rico, and residents of the outlying areas under U.S. sovereignty or
jurisdiction (principally American Samoa, Guam, Virgin
Islands of the United States, and Commonwealth of the
Northern Mariana Islands). The definition of residence
conforms to the criterion used in the census, which defines
a resident of a specified area as a person ‘‘usually resident’’
in the area. For the most part, ‘‘usual residence’’ is defined
subjectively by the census respondent; it is not defined by
de facto presence in a particular house or dwelling, nor is
it defined by any de jure (legal) basis for a respondent’s
presence. There are two exceptions to the subjective
character of the residence definition.
1. Persons living in military barracks, prisons, and some
other types of residential group-quarters facilities are
generally reported by the administration of their facility,
and their residence is generally the location of the
facility. Naval personnel aboard ships reside at the
home port of the ship, unless the ship is deployed to
the overseas fleets, in which case they are not U.S.
residents. Exceptions may occur in the case of Armed
D–4
Forces personnel who have an official duty location
different from the location of their barracks, in which
case their place of residence is their duty location.
2. Students residing in dormitories report their own residences, but are instructed on the census form to report
their dormitories, rather than their parental homes, as
their residences.
The universe of interest to the official estimates of
population that the Census Bureau produces is the resident population of the United States, as it would be
counted by the last census (1990) if this census had been
held on the reference date of the estimates. However, this
universe is distinct from the census universe in two regards,
neither of which affects the total population.
1. The estimates differ from the census in their definition
of race, with categories redefined to be consistent with
other administrative-data sources. Specifically, persons enumerated in the 1990 census who gave responses
to the census race question not codable to White,
Black, American Indian, Eskimo, Aleut, or any of
several Asian and Pacific Islander categories were
assigned a race within one of these categories. Most
of these persons (9.8 million nationally) were persons
of Hispanic origin who gave a Hispanic origin response
to the race question. Publications from the 1990
census do not incorporate this modification, while all
official population estimates incorporate it.
2. Respondents’ ages are modified to compensate for
the aging of census respondents from the census date
(April 1, 1990) to the actual date that the census form
was completed. This modification was necessitated by
the fact that no printed instruction on the census form
required respondents to state their age as of the
census date. They therefore tended to state their age
at the time of completion of the form, or possibly their
next anticipated birthday. This modification is not included
in census publications, which tabulate respondents’
age as stated. It is applied to all population estimates
and projections, including controls for the CPS.
The universe underlying the CPS sample is confined to
the civilian noninstitutional population. Thus, independent
population controls are sought for this population, which
defines the CPS control universe. The previously mentioned alternative definitions of race and age, associated
with the universe for official estimates, are carried over to
the CPS control universe. Four additional changes are
incorporated, which distinguish the CPS control universe
from the universe for official estimates.
1. The CPS control universe excludes active-duty Armed
Forces personnel, including those stationed within the
United States, while these are included in the resident
population. ‘‘Active duty’’ is taken to refer to personnel
reported in military strength statistics of the Departments of Defense (Army, Navy, Marines, and Air
Force) and Transportation (Coast Guard), including
reserve forces on 3- and 6-months’ active duty for
training, National Guard reserve forces on active duty
for 4 months or more, and students at military academies. Reserve forces not active by this definition are
included in the CPS control universe, if they meet the
other inclusion criteria.
2. The CPS control universe excludes persons residing
in institutions, such as nursing homes, correctional
facilities, juvenile detention facilities, and long-term
mental health care facilities. The resident population,
on the other hand, includes the institutional population.
3. The CPS control universe, like the resident population
base for population estimates, includes students residing in dormitories. Unlike the population estimates
base, however, it accepts as state of residence a
family home address within the United States, in
preference to the address of the dormitory. This affects
estimation procedures at the state level, but not at the
national level.
4. An added difference between the census official
estimates and the CPS population controls after January 1, 1994, is that the former do not include adjustment for undercoverage in the 1990 census based on
the Post-Enumeration Survey (PES) while the latter
do. Details of this adjustment are given later in this
appendix.2
CALCULATION OF POPULATION PROJECTIONS
FOR THE CPS UNIVERSE
The present section is concerned with the methodology
for computing population projections for the CPS control
universe used in the actual calibration of the survey. Three
subsections are devoted to the calculation of (i) the total
population, (ii) its distribution by age, sex, race, and
Hispanic origin, and (iii) its distribution by state of residence.
Total Population
Projections of the population to the CPS control reference date (the first of each month) are determined by a
base population (either estimated or enumerated) and a
2
Whether the presence of an adjustment for census undercount
implies an actual change in universe, as opposed to the correction of a
measurement problem, is a legitimate subject for dispute. In the context of
the census itself, adjustment might be viewed as a measurement issue
relative to the population of a ‘‘true’’ universe of U.S. residents. It is treated
here as a technical attribute of the universe, since unadjusted population
estimates are specifically adapted to remain consistent with the census in
regard to coverage. Hence ‘‘census-level’’ and ‘‘adjusted’’ define distinct
population universes.
D–5
projection of population change. The latter is generally an
amalgam of components measured from administrative
data and components determined by projection. The ‘‘balancing equation of population change’’ used to produce
estimates and projections of the U.S. population states the
population at some reference date as the sum of the base
population and the net of various components of population
change. The components of change are the sources of
increase or decrease in the population from the reference
date of the base population to the date of the estimate. The
exact specification of the balancing equation depends on
the population universe, and the extent to which components of population change are disaggregated. For the
resident population universe, the equation in its simplest
form is given by
Rt1 ⫽ Rt0 ⫹ B ⫺ D ⫹ NM
(1)
where:
Rt1 = Resident population at time t1 (the reference
date),
Rt0 = Resident population at time t0 (the base date),
B
= births to U.S. resident women, from time t0 to time
t1,
D
= deaths of U.S. residents, from time t0 to time t1,
NM = net migration to the United States (migration into
the United States minus migration out of the
United States) from time t0 to time t1.
It is essential that the components of change in a
balancing equation represent the same population universe as the base population. If we derive the current
population controls for the CPS independently, from a base
population in the CPS universe, civilian noninstitutional
deaths would replace resident deaths, ‘‘net migration’’
would be confined to civilian migrants, but would incorporate net recruits to the Armed Forces and net admissions to
the institutional population. The equation would thus appear
as follows:
CNPt1 ⫽ CNPt0 ⫹ B ⫺ CND ⫹(NCM ⫺ NRAF ⫺ NEI)
where:
CNPt1
CNPt0
B
=
=
=
CND
=
NCM
NRAF
=
=
NEI
=
(2)
civilian noninstitutional population, time t1,
civilian noninstitutional population, time t0,
births to the U.S. resident population, time t0
to time t1,
deaths of the civilian noninstitutional population, time t0 to time t1,
net civilian migration, time t0 to time t1,
net recruits (inductions minus discharges) to
the U.S. Armed Forces from the domestic
civilian population, from time t0 to time t1,
net admissions (enrollments minus discharges)
to institutions, from time t0 to time t1.
In the actual estimation process, it is not necessary to
directly measure the variables CND, NRAF, and NEI in the
above equation. Rather, they are derived from the following
equations:
CND ⫽ D ⫺ DRAF ⫺ DI
(3)
where D = deaths of the resident population, DRAF =
deaths of the resident Armed Forces, and DI = deaths of
inmates of institutions (the institutional population)
NRAF ⫽ WWAFt1 ⫺ WWAFt0 ⫹ DRAF ⫹ DAFO ⫺ NRO
(4)
where WWAFt1 and WWAFt0 represent the U.S. Armed
Forces worldwide at times t1 and t0, DRAF represents
deaths of the Armed Forces residing in the United States
during the interval, DAFO represents deaths of the Armed
Forces residing overseas, and NRO represents net recruits
(inductions minus discharges) to the Armed Forces from
the overseas civilian population
NEI ⫽ CIPt1 ⫺ CIPt0 ⫹ DI
(5)
where CIPt1 and CIPt0 represent the civilian institutional
population at times t1 and time t0, and DI represents
deaths of inmates of institutions during the interval. The
appearance of DI in both equations (3) and (5) with
opposite sign, and DRAF in equations (3) and (4) with
opposite sign, ensures that they will cancel each other
when applied to equation (2). This fact obviates the need to
measure institutional inmate deaths or deaths of the Armed
Forces residing in the United States.
A further disaggregation to facilitate the description of
how the components on the right side of equation (2) are
measured as follows:
NCM ⫽ IM ⫹ CCM
(6)
where IM = net international migration, and CCM = net
movement of civilian citizens to the United States from
abroad, in the time interval (t0,t1).
At this point, we restate equation (2) to incorporate
equations (3), (4), (5), and (6), and simplify. The resulting
equation, which follows, is the operational equation for
population change in the civilian noninstitutional population
universe.
CNPt1 ⫽ CNPt0 ⫹ B ⫺ D ⫹ 共IM ⫹ CCM兲 ⫺
共WWAFt1 ⫺ WWAFt0 ⫹ DAFO ⫺ NRO兲 ⫺ 共CIPt1 ⫺ CIPt0兲
(7)
Equation (7) identifies all the components that must be
separately measured or projected to update the total
civilian noninstitutional population from a base date to a
later reference date.
Aside from forming the procedural basis for all estimates
and projections of total population (in whatever universe),
balancing equations (1), (2), and (7) also define a recursive
concept of internal consistency of a population series
within a population universe. We consider a time series of
population estimates or projections to be internally consistent if any population figure in the series can be derived
D–6
from any earlier population figure as the sum of the earlier
population and the components of population change for
the interval between the two reference dates.
Measuring the Components of the Balancing
Equation
Balancing equations such as equation (7) requires a
base population, whether estimated or enumerated, and
information on the various components of population change.
Because a principal objective of the population estimating
procedure is to uphold the integrity of the population
universe, the various inputs to the balancing equation need
to agree as much as possible with respect to geographic
scope, treatment of underenumeration of population or
underregistration of events, and residential definition, and
this is a major aspect of the way they are measured. A
description of the individual inputs to equation (7), which
applies to the CPS control universe, and their data sources
follows.
The census base population (CNPt0). While any base
population (CNPt0, in equation (7)), whether a count or an
estimate, can give rise to estimates and projections for
later dates, the original base population for all unadjusted
postcensal estimates and projections is the enumerated
population from the last census. Although survey controls
are currently adjusted for underenumeration in the 1990
census, the census population is itself not adjusted for
underenumeration in the census; rather, an adjustment
difference is added in a later step. After the 2000 census, it
is expected that official estimates will be adjusted for
undercoverage, so that the adjustment can be incorporated
in the base population. However, the census base population does include minor revisions arising from count resolution corrections. As these corrections arise entirely from
the tabulation of data from the enumerated population, they
are unrelated to adjustment for underenumeration. As of
the end of 1994, count resolution corrections incorporated
in the estimates base population amounted to a gain of
8,418 persons from the originally published count.
In order to apply equation (7), which is derived from the
balancing equation for the civilian noninstitutional population, the resident base population enumerated by the
census must be transformed into a base population consistent with this universe. The transformation is given as
follows:
CNP0 ⫽ R0 ⫺ RAF0 ⫺ CIP0
(8)
where R0 = the enumerated resident population, RAF0 =
the Armed Forces resident in the United States on the
census date, and CIP0 = the civilian institutional population
residing in the United States on the census date. This is
consistent with previous notation, but with the stipulation
that t0 = 0, since the base date is the census date, the
starting point of the series. The transformation can also be
used to adapt any postcensal estimate of resident population to the CPS universe for use as a population base in
equation (7). In practice, this is what occurs when the
monthly CPS population controls are produced.
Adjustment for underenumeration, while essential to the
CPS universe, is handled independently of the process of
producing projections. It is added after the population is
updated. Hence, adjustment will be discussed later in this
appendix.
Births and deaths (B, D). In estimating total births and
deaths of the resident population (B and D, in equation (7)),
we assume the population universe for vital statistics to
match the population universe for the census. If we define
the vital statistics universe to be the population subject to
the natural risk of giving birth or dying, and having the
event recorded by vital registration systems, the assumption implies the match of this universe with the census-level
resident population universe. We relax this assumption in
the estimation of some characteristic detail, and this will be
discussed later in the appendix.
The numbers of births and deaths of U.S. residents are
supplied by the National Center for Health Statistics (NCHS).
These are based on reports to NCHS from individual state
and local registries; the fundamental unit of reporting is the
individual birth or death certificate. For years in which
reporting is considered final by NCHS, the birth and death
statistics are considered final; these generally cover the
period from the census until the end of the calendar year 3
years prior to the year of the CPS series. For example, the
last available final birth and death statistics available in
time for the 1998 CPS control series were for calendar year
1995. Final birth and death data are summarized in NCHS
publications (see Ventura et al., 1997a, Anderson et al.,
1997). Monthly births and deaths for calendar year(s) up to
2 years before the CPS reference dates (e.g., 1996 for
1998 controls) are based on provisional estimates by
NCHS (Ventura et al., 1997b). For the year before the CPS
reference date through the last month before the CPS
reference date, births and deaths are projected based on
the population by age according to current estimates and
age-specific rates of fertility and mortality and seasonal
distributions of births and deaths observed in the preceding
year.
Various concerns exist relative to the consistency of the
NCHS vital statistics universe with the census resident
universe and the CPS control universe derived from it.
1. Births and deaths can be missed entirely by the
registration system. In the accounting of the national
population without adjustment for underenumeration,
underregistration of vital events is tacitly assumed to
match underenumeration in the census. While we
have found no formal investigation of the underregistration of deaths, we surmise that it is likely to be far
less than the rate of underenumeration in the census
because of the requisite role of local public agencies in
D–7
the processing of bodies for cremation or burial. A test
of the completeness of birth registration conducted by
the Census Bureau in 1964-1968, applied to the
distribution of 1992 births by race and whether born in
hospital, implied an underregistration of 0.7 percent,
far less than most estimates of underenumeration in
recent censuses, but probably greater than the underregistration of deaths.
2. Birth and death statistics obtained from NCHS exclude
those events occurring to U.S. residents while outside
the United States. The resulting slight downward bias
in the numbers of births and deaths would be partially
compensatory.
These two sources of bias, which are assumed to be
minor, affect the accounting of total population. Other, more
serious comparability problems exist in the consistency of
NCHS vital statistics with the CPS control universe, with
respect to race, Hispanic origin, and place of residence
within the United States. These will be discussed in later
sections of this appendix.
International migration (IM). The objective of the current
procedures to estimate international migration (IM, in equation (7)), is to transform the number of legal immigrants—
for whom records of migration are available—into an
estimate of the net of persons who become ‘‘usual residents’’ of the United States according to the census
residency definition, and those who cease to be usual
residents because they move out of the United States. This
objective is met primarily by the availability of administrative data from Federal agencies. However, it is beset by
five fundamental problems.
1. A substantial number of foreign-born persons increase
the resident population each year, either by overstaying legal, nonimmigrant visas or by entering the country without inspection. Those that have not returned to
their country of origin (either voluntarily or by force of
law) by a CPS date are eligible for interview by the
CPS and must be included in the CPS controls.
2. Because the geographic limits of the census universe
do not include Puerto Rico or outlying areas under
U.S. jurisdiction, persons who enter or leave the
country from or to these areas must be treated as
international migrants. However, migrants to or from
these areas are generally U.S. citizens and need not
produce any administrative record of their moves.
3. Legal residents of the United States departing for
residence abroad are not required to provide any
administrative record of departure. Hence, there exist
no current data on emigration.
4. Some persons who enter the country legally, but
temporarily (e.g., foreign students, scholars, and members of certain professions), are eligible for enumeration in the census as usual residents and should be
included in population estimates. While administrative
records exist for the arrival of such persons, there is no
adequate source of data for their departures or their
lengths of stay.
5. The census definition of usual residence is subjective
and depends on the interpretation of census respondents. Immigration data generally assume a legal,
rather than a subjective concept of residence (e.g.,
legal permanent resident, nonresident alien) that does
not match the census concept. Thus, an alien could be
legally resident in the United States, but perceive their
usual residence to be elsewhere when responding to a
census or survey.
For purposes of population accounting, migration to the
United States is assumed to include persons arriving in the
United States who, based on their arrival status, appear to
be new census-defined residents, meaning they would
report their usual residence in a census as being inside the
United States. The accounting should thus include all legal
permanent residents, refugees, and undocumented migrants
that do not return and are not deported. It should also
include arrivals (such as foreign students and foreign
scholars) that generally assume steady residence in the
United States for the duration of their stay and would,
therefore, be enumerated in a census. Tourists and business travelers from abroad are assumed not to be U.S.
residents and are, therefore, excluded from the international migration tally.
The core data source for the estimation of international
migration is the Immigration and Naturalization Service
(INS) public use immigrant file, which is issued yearly for
immigrants (year defined by the Federal fiscal year). This
file contains records for citizens of foreign countries immigrating or establishing legal permanent residence in the
United States and accounts for the majority of international
migration to the United States. Data from the immigrant file
are summarized by the INS in its statistical yearbooks (for
example, U.S. Immigration and Naturalization Service,
1997). However, the file contains no information on emigration, undocumented immigration, or nonimmigrant moves
into the country. Moreover, immigrants do not always
change residence at time of immigration; they may in fact
already reside in the United States. INS immigrant data
are, therefore, partitioned into four categories based on
INS class of admission and each category is treated
differently.
The first category consists of all persons classed as
‘‘new arrivals,’’ meaning their last legal entry into the U.S.
coincided with their immigration or acquisition of legal
permanent resident status. These persons are simply
included in the migration component for the month and
year of their immigration.
The second category consists of persons already resident in the United States who are adjusting their status
from nonimmigrant (e.g., temporary resident) to immigrant
D–8
and who are not refugees. They include a large component
of persons who are nonimmigrant spouses or children of
U.S. citizens or legal permanent residents. We cannot
account for these persons at their time of arrival through
current data. We could account for these persons retrospectively at the earlier year of their arrival from INS
records. However, this would result in a serious downward
bias for the most recent years, since we would miss similar
persons, currently entering the United States, who will
immigrate in future years. For this reason, we accept the
number of adjustees in this category as a proxy for the
number of future adjustees physically entering the country
in the current year. This assumption is robust over time
provided the number of persons in this category remains
stable from year to year.3
A third category consists of persons who entered the
country as refugees and are currently adjusting their status
to immigrant. The assumption of year-to-year stability of
the flow, made for the previous category, would generally
be inadequate for refugees because they tend to enter the
country in ‘‘waves’’ depending on emigration policies or
political upheavals in foreign countries. Hence, the time
series of their entry would be poorly reflected by the time
series of their conversion to immigrant status. The Office of
Refugee Resettlement (ORR) maintains monthly data on
the arrival of refugees (as well as some special-status
entrants from Haiti and Cuba) by country of citizenship.
Refugees adjusting to immigrant status are thus excluded
from the accounting of immigrants but included as refugees
at their time of arrival based on the ORR series.
The fourth and final category consists of persons living
in the country illegally—either as nonimmigrant visa overstayers or ‘‘entered without inspection’’—who adjust to
legal immigrant status by proving a history of continuous
residence in the United States. Since fiscal year 1989, the
largest portion of these persons has been admitted under
the Immigration Reform and Control Act of 1986 (IRCA),
which required proof of permanent residence since 1982.
Generally, these persons should qualify as census-defined
residents from the date of their arrival, since they do not
generally expect to return to their country of origin. Because
their arrival occurred before the 1990 census and current
undocumented migration is separately accounted, immigrating IRCA adjustees are excluded from the accounting
of migration altogether for post-1990 estimates.
The separate accounting of net undocumented migration cannot be based on registration data because, by
definition, it occurs without registration. Research conducted at the Census Bureau (Robinson, 1994) has produced an allowance of 225,000 net migration per year,
3
In fiscal year 1995, the assumption of a stable flow of nonimmigrants
adjusting to immigrant status was seriously undermined by a legal
change. For the first time, persons qualifying for legal immigrant status
were allowed to do so without leaving the country. As a result, a rapid
increase in applications to INS resulted in an increase in the backlog of
unprocessed applications. It was, therefore, necessary to adapt the
accounting for the increase in applications, rather than assuming a
one-for-one relationship of current adjustees to future adjustees currently
entering the country.
based on observation of the late 1980s, with the universe
confined (appropriately) to those counted in the 1990
census. This number is a consensus estimate, based partly
on a specific derivation and partly on a review of other
estimates for the same period. The derivation is based on
a residual method, according to which the number of
foreign-born persons enumerated in 1990 by period of
arrival is compared to net legal migration measured for the
same period. Currently, all Census Bureau estimates and
projections of the population after 1990 incorporate this
annual allowance as a constant.
The remaining category of foreign-born migration to the
United States, not included in the immigrant data, is the
flow of legal temporary residents; persons who reside in
the country long enough to consider themselves ‘‘usual
residents’’ while they are here, but who do not have
immigrant visas. These include foreign students, scholars,
some business persons (those who establish residence),
and some professionals who are provided a special allowance to work. INS data on nonimmigrants would provide
records of admission for temporary visa holders; however,
there would be no reliable data source for their departures.
The stock of this category of foreign-born persons enumerated in the 1990 census was estimated at 488,000, based
on various characteristics measured by the census ‘‘long
form’’ (1 in 6 households). This migration flow is currently
assumed to maintain this stock at 488,000, This tells us
that net migration (arrivals minus departures) equals the
estimated number of deaths occurring to the group while in
the United States. The overall net migration of this group is
rather trivial (less than a thousand per year); far more
important is the implied distribution of migration by age,
which will be discussed later in this appendix.
A major migratory movement across the U.S. frontier,
not directly measurable with administrative data, is the
emigration of legal permanent residents of the United
States to abroad. The current projection is a constant
222,000 per year, of whom 195,000 are foreign-born
(Ahmed and Robinson, 1994). Like the allowance for
undocumented immigration, the method of derivation is a
residual method. In this case, the comparison is between
foreign-born persons enumerated in the 1980 census and
foreign-born persons enumerated in the 1990 census who
gave a year of arrival prior to 1980. In theory, the latter
number would be smaller, with the difference attributed to
either death or emigration. The number of deaths was
estimated through life tables, leaving emigration as a
residual. This analysis was carried out by country of birth,
facilitating detailed analysis of possible biases arising from
differential reporting in the two censuses. The annual
estimate of 195,000 was projected forward as a constant,
for purposes of producing post-1990 estimates and projections.
The remaining annual emigrant allowance of 27,000 is
based on an estimate of native-born emigrants carried out
during the early 1980s which employed data on U.S.-born
D–9
persons enumerated in foreign censuses, as well as historical evidence from the period before 1968, when permanent departures from the United States were registered by
INS.
A final class of international migration that can only be
estimated roughly as a net flow is the movement of persons
from Puerto Rico and the outlying areas to the United
States. No definitive administrative data exist for measuring volume, direction, or balance of these migration flows.
The migratory balance from all areas except Puerto Rico is
assumed to be zero. For Puerto Rico, we assume an
allowance of roughly 7,000 net migration (arrivals minus
departures) per year. This projection is based on an
imputation of net out-migration from Puerto Rico during the
1980s, based on a comparison of the island’s natural
increase (births minus deaths) with the population change
between the 1980 and 1990 censuses.
There is considerable variation in the currency of data
on international migration, depending on the source. The
INS public use immigrant file is generally available during
the summer following the fiscal year of its currency, so
projections for control of the CPS have access to final
immigration data through September, 2 years prior to the
reference date of the projections (e.g., 1995 for 1997
controls). INS provides a provisional monthly series for the
period from October through June for the following year,
that is, through the middle of the calendar year before the
CPS reference date. From that point until the CPS reference date itself, the immigrant series is projected. The
Office of Refugee Resettlement data on refugees follows
roughly the same timetable, except the preliminary series
is ongoing and is generally current through 4 to 5 months
before the CPS reference date. Net undocumented immigration, emigration of legal residents, and net migration
from Puerto Rico are projected as constants from the last
decennial census, as no current data are available. The
determination of the base series for the projection requires
considerable research, so the introduction of data for a new
decade generally does not occur until about the middle of
the following decade.
Net migration of Federally affiliated civilian U.S. citizens (CCM). While the approach to estimating international migration is to measure the migration flows for
various types of migration, no data exist on flows for civilian
U.S. citizens between the United States and abroad (except
with respect to Puerto Rico, which is treated here as
international); this is the variable CCM in equation (7).
Because U.S. citizens are generally not required to report
changes of address to or from other countries, there is no
registration system from which to obtain data. Fortunately,
there are data sources for measuring the current number
(or stock) of some classes of civilian U.S. citizens residing
overseas, including persons affiliated with the Federal
government, and civilian dependents of Department of
Defense employees, both military and civilian. The estimation procedure used for the movement of U.S. citizens thus
relies on an analysis of the change in the stock of overseas
U.S. citizens over time. We assume the net migration of
non-Federally affiliated U.S. citizens to be zero.
Imputation of migration from stock data on overseas population is by the following formula:
CCM ⫽ ⫺ (OCPt1 ⫺ OCPt0 ⫺ OB ⫹ OD)
(9)
where OCPt1 and OCPt0 represent the overseas civilian
citizen population (the portion that can be estimated) at the
beginning of two consecutive months; OB is the number of
births, and OD the number of deaths of this population
during the month. This equation derives from the balancing
equation of population change and states the net migration
to the overseas civilian population to be its total change
minus its natural increase.
Federally affiliated civilian U.S. citizens residing overseas for whom we have data, or the basis for an estimate,
are composed of three categories of persons; dependents
of Department of Defense (DOD) personnel, civilian Federal employees, and dependents of non-DOD civilian Federal employees. The number of civilian dependents of DOD
military and civilian personnel overseas are published
quarterly by DOD; the number of Federal employees—total
and DOD civilian—located overseas at the beginning of
each month is published by the Office of Personnel Management (OPM). We estimate the number of dependents of
non-DOD civilian Federal employees by assuming the ratio
of dependents to employees for non-DOD personnel to
match the ratio for civilian DOD personnel. Quarterly data
(e.g., DOD dependents) are converted to monthly by linear
interpolation.
Estimates of the numbers of births and deaths of this
population (OB and OD, in equation (9)) depend almost
entirely on a single statistic, published quarterly by DOD:
the number of births occurring in United States military
hospitals overseas. This number (apportioned to months
by linear interpolation) is adopted as an estimate of overseas births, on the reasoning that most births to Federally
affiliated citizens or their dependents would occur in military hospitals. We estimate deaths by assuming deaths of
persons 1 year of age or older to be nil; we estimate infant
deaths by applying a life table mortality rate to the number
of births. The likely underestimate of both births and deaths
would tend to compensate each other. While this method of
estimating the natural increase of overseas civilian citizens
is very approximate, the numbers involved are very small,
so the error is unlikely to have a serious effect on estimates
of civilian citizen migration.
Data from DOD and OPM used to estimate civilian
citizen migration are generally available for reference dates
until 6 to 9 months prior to the CPS reference date. For the
months not covered by data, the estimates rely on projected levels of the components of the overseas population
and overseas births.
D–10
Net recruits to the Armed Forces from the civilian
population (NRAF). The net recruits to the worldwide U.S.
Armed Forces from the U.S. resident civilian population is
given by the expression
(WWAFt1 - WWAFt0 + DRAF + DAFO - NRO)
in equation (4). The first two terms represent the change in
the number of Armed Forces personnel worldwide. The
third and fourth represent deaths of the Armed Forces in
the U.S. and overseas, respectively. The fifth term represents net recruits to the Armed Forces from the overseas
civilian population. While this procedure is indirect, it allows
us to rely on data sources that are consistent with our
estimates of the Armed Forces population on the base
date.
Most of the information required to estimate the components of this expression is supplied directly by the Department of Defense, generally through a date 1 month prior to
the CPS control reference date; the last month is projected,
for various detailed subcomponents of military strength.
The military personnel strength of the worldwide Armed
Forces, by branch of service, is supplied by personnel
offices in the Army, Navy, Marines and Air Force, and the
Defense Manpower Data Center supplies total strength
figures for the Coast Guard. Participants in various reserve
forces training programs (all reserve forces on 3- and
6-month active duty for training, National Guard reserve
forces on active duty for 4 months or more, and students at
military academies) are treated as active-duty military for
purpose of estimating this component, and all other applications related to the CPS control universe, although the
Department of Defense would not consider them to be in
active duty.
The last three components of net recruits to the Armed
Forces from the U.S. civilian population, deaths of the
resident Armed Forces (DRAF), deaths of the Armed
Forces overseas (DAFO) and net recruits to the Armed
Forces from overseas (NRO) are usually very small and
require indirect inference. Four of the five branches of the
Armed Forces (those in DOD) supply monthly statistics on
the number of deaths within each service. Normally, deaths
are apportioned to the domestic and overseas component
of the Armed Forces relative to the number of the domestic
and overseas military personnel. If a major fatal incident
occurs for which an account of the number of deaths is
available, these are assigned to domestic or overseas, as
appropriate, before application of the pro rata assignment.
Lastly, the number of net recruits to the Armed Forces from
the overseas population (NRO) is computed annually, for
years from July 1 to July 1, as the difference between
successive numbers of Armed Forces personnel giving a
‘‘home of record’’ outside the 50 states and District of
Columbia. To complete the monthly series, the ratio of
persons with home of record outside the U.S. to the
worldwide military is interpolated linearly, or extrapolated
as a constant from the last July 1 to the CPS reference
date. These monthly ratios are applied to monthly worldwide Armed Forces strengths; successive differences of
the resulting estimates of persons with home of record
outside the U.S. yield the series for net recruits to the
Armed Forces from overseas.
Change in the institutional population (CIPt1 - CIPt0).
The change in the civilian population residing in institutions is measured by a report of selected group quarters
facilities carried out by the Census Bureau, in conjunction
with state governments, through the Federal State Cooperative Program for Population Estimates (FSCPE). Information is collected on the type of facility and the number of
inhabitants, and these data are tabulated by institutional
and noninstitutional. The change in the institutional population from the census date to the reference date of the
population estimates, measured by the group quarters
report, is used to update the institutional population enumerated by the census. Certain facilities, such as military
stockades and resident hospitals on military bases, are
excluded from the institutional segment, as most of their
inhabitants would be military personnel; hence, the resulting institutional population is assumed to be civilian.
The last available group quarters data refer to July 1,
2 years prior to the reference year of the CPS series
(July 1, 1995, for 1997 CPS controls). Institutional population from this date forward must be projected. Censusbased institutional participation rates are computed for the
civilian population by type of institution, age, sex, race, and
Hispanic origin. These are used to produce estimates for
the group quarters report dates, which are then proportionally adjusted to sum to the estimated totals from the report.
Participation rates are recomputed, and the resulting rates,
when applied to the monthly estimate series for the civilian
population by characteristic, produce projections of the
civilian institutional population. These estimates produce
the last two terms on the right side of equation (7).
Population by Age, Sex, Race, and Hispanic
Origin
The CPS second-stage weighting process requires the
independent population controls to be disaggregated by
age, sex, race, and Hispanic origin. The weighting process,
as currently constituted, requires cross-categories of age
group by sex and by race, and age group by sex and by
Hispanic origin, with the number of age groups varying by
race and Hispanic origin. Three categories of race (White,
Black, and all Other4), and two classes of ethnic origin
(Hispanic, non-Hispanic) are required, with no cross-classifications
of race and Hispanic origin. Beginning in 1993, the population projection program adopted the full cross-classification
of age by sex and by race and by Hispanic origin into its
4
Other includes American Indian, Eskimo, Aleut, Asian, and Pacific
Islander.
D–11
monthly series, with 101 single-year age categories (single
years from 0 to 99, and 100 and over), two sex categories
(male, female), four race categories (White; Black; American Indian, Eskimo, and Aleut; and Asian and Pacific
Islander), and two Hispanic origin categories (not Hispanic,
Hispanic).5 The resulting matrix has 1,616 cells (101 x 2 x
4 x 2), which are then aggregated to the distributions used
as controls for the CPS.
In discussing the distribution of the projected population
by characteristic, we will stipulate the existence of a base
population and components of change, each having a
distribution by age, sex, race, and Hispanic origin. In the
case of the components of change, ‘‘age’’ is understood to
be age at last birthday as of the estimate date. The present
section on the method of updating the base population with
components of change is followed by a section on how the
base population and component distributions are measured.
active-duty resident Armed Forces, and the civilian institutional population is precisely the same for any age group as
for the total population, so we omit its discussion. Of
course, it is necessary to know the age distribution of the
resident Armed Forces population and the civilian institutionalized population in order to derive the CNP by age
from the resident population by age.
Under ideal circumstances, a very standard demographic procedure—the method of cohort components—would
be employed to update the population age distribution. The
cohort component method yields the population of a given
birth cohort (persons born in a given year) from the same
cohort in an earlier year, incremented or decremented by
the appropriate components of change. To take a simple
case, we assume the time interval to be 1 year, from the
beginning of year t to the beginning of year t+1, and the age
distribution being updated is a distribution of the resident
population by single year of age. We can derive the number
of persons age x (for most values of x) by the equation
Rx,t⫹1 ⫽ Rx⫺1,t ⫺ Dx,t ⫹ NMx,t
Update of the Population by Sex, Race, and
Hispanic Origin
The full cross-classification of all variables except age
amounts to 16 cells, defined by variables not naturally
changing over time (2 values of sex by 4 of race by 2 of
Hispanic origin). The logic for projecting the population of
each of these cells follows the same logic as the projection
of the total population and involves the same balancing
equations. The procedure, therefore, requires a distribution
by sex, race, and Hispanic origin to be available for each
component on the right side of equation (7). Similarly, the
derivation of the civilian noninstitutional base population
from the census resident population follows equation (8).
Update of the Age Distribution: The
Inflation-Deflation Method
Having produced population series for 16 cells of sex,
race, and Hispanic origin, it remains to distribute each cell
to 101 age groups. Age differs fundamentally from other
demographic characteristics because it changes over time.
The balancing equations described in earlier sections rely
on the premise that the defining characteristics of the
population being estimated remain fixed, hence the procedure must be adapted to allow for the fact that age does not
meet this criterion. We present the method of estimating
the age distribution through the equation for the resident
population, analogous to equation (1) for the total population, for reasons discussed at the end of this section. The
mathematical logic producing the civilian noninstitutional
population (CNP) by age from the resident population, the
5
Throughout this appendix, ‘‘American Indian’’ refers to the aggregate
of American Indian, Eskimo, and Aleut; ‘‘API’’ refers to Asian and Pacific
Islander.
where:
Rx,t+1
Rx-1,t
Dx,t
NMx,t
(10)
=
resident population aged x (last birthday), at
time t+1
= resident population aged x-1, at time t
= deaths during the time interval from t to t+1,
of persons who would have been age x at
time t+1
= net migration during the time interval from t
to t+1, of persons who would be age x at
time t+1
For the special case where x=0, the equation (10) becomes
R0,t⫹1 ⫽ Bt ⫺ D0,t ⫹ NM0,t
(11)
with variable definitions analogous to equation (10), except
that Bt equals live births during the time interval from t to
t+1. For the special case where the population estimated
comprises an ‘‘open’’ age category, like 100 and over, the
equation becomes
RX,t⫹1 ⫽ Rx⫺1,t ⫹ RX,t⫺ DX,t ⫹ NMX,t
(10)
where RX,t+1 and RX,t designate persons age x and older, at
time t+1 and time t, respectively, and DX,t and NMX,t
designate deaths and net migration, respectively, from t to
t+1 of persons who would be age x and older at time t+1.
Here, Rx-1,t is defined as in equation (10).
A fundamental property of this method is its tracking of a
birth cohort, or persons born in a given year, to a later date
through aging; hence it optimizes the comparability of the
population of birth cohorts over time. But, in producing a
time series of projections or estimates of a population age
distribution, the objective is rather to maintain consistency
from month-to-month in the size of age groups, or the
number of persons in a specific, unchanging age range. As
D–12
a result of this, the cohort component method of updating
age distributions is highly sensitive to discontinuities from
age group to age group in the base population. Unfortunately, two attributes of the census base population render
the assumption of continuity across age groups untenable.
First, underenumeration in the census is highly age-specific.
This problem is most egregious for age categories of Black
males in the age range from 25 to 40, which represent a
‘‘trough’’ in census coverage. It is lowest near the middle of
the age range, as is evident from the age pattern of
census-based sex ratios within the range. Consequently,
consecutive years of age are inconsistent with respect to
the proportion of persons missed by the census. The
second troublesome attribute of the census base population is the presence of distinct patterns of age reporting
preference, as manifested by a general tendency to underreport birth years ending in one and overreport birth years
ending in zero. Because the census date falls in the first
half of a decade year, this results in an exaggeration of the
number of persons with age ending in nine, and an
understatement of the number with age ending in eight.
Were the cohort component method applied to this distribution without adaptation, these perturbations in the age
distribution would progress up the age distribution with the
passage of time, impairing the consistency of age groups in
the time series.
The solution to this problem, first adopted in the 1970s,
is an adaptation of the cohort-component method that
seeks to prevent the aging of spurious elements of the
base population age structure. The resulting method is
known as ‘‘inflation-deflation.’’ In principle, the method
depends on the existence of an alternative base population
distribution by age, sex, race, and origin, for the census
date, which is free of age-specific underenumeration and
inconsistent age reports, but as consistent as possible with
the census in every other aspect of the population universe. Under ideal circumstances, the alternative base
distribution would be the population that would have been
enumerated by the census, if the census had been free of
undercount and age misreporting, although this requirement is neither realistic nor essential. It is essential,
however, that single-year-of-age categories be consistent
with one another, in the sense that they share the same
biases. The correctness of the overall level of the alternative base population (absence of biases in the total population of all ages) is unimportant. Once such a base
population distribution is available, it can be updated,
recursively, from the census date to the estimates reference date using the cohort component logic of equations
(10), (11), and (12) without concern for bias arising from
discontinuities in the population by age.
To update a census-level population from one date to
another, ‘‘inflation-deflation factors’’ are computed as the
ratio of each single-year age group in the census base
population to the same age group in the alternative base
population. If the alternative population represents a higher
degree of coverage than the census base population, the
factors can be expected to have values somewhat less
than one. If the alternative population is free of age
misreporting, then overreported ages in the census may
have factors greater than one, while underreported ages
will have factors less than one. If, as in the present
application, the calculations are done within categories of
sex, race, and Hispanic origin, the factors can take on
unnatural values if there is relative bias between the
census and the alternative population in the reporting of
these variables. Such is the case with the current application for ‘‘all other races,’’ the racial category combining
American Indian, Eskimo, Aleut, Asian, and Pacific Islanders. This group tends to be more heavily reported in the
census than in other administrative data sources.
The census-level population at the base date is ‘‘inflated,’’
through multiplication by the reciprocal of the factors, to be
consistent with the alternative population base. The resulting distribution is updated via cohort component logic
(equations (10) through (12)), and the factors are multiplied
by the same age groups (not the same birth cohorts) on the
reference date, thereby deflating the estimates back to
‘‘census level.’’ Because the inflation-deflation factors are
held constant with respect to age rather than birth cohort,
those aspects of the base age distribution that should not
increase in age from year-to-year are embodied in the
inflation-deflation factors, and those aspects that should
indeed ‘‘age’’ over time are embodied in the alternative
base distribution. A mathematical representation of this
procedure is given by the following equation, analogous to
equation (10) for the cohort component method
(
A
Rx,0 Rx—1,0
A
A
A
´
R
⫽
R
— Dx,t
⫹ NMx,t
⫹ NMx,t
x,t⫹1
A
Rx⫺1,0 x—1,t
Rx,0
)
(13)
´
where R
x,t⫹1 is the census-level estimate of population age
´
A
x at time t+1, R
x⫺1,tis the same for age x-1 at time t, Rx,0 is
the resident population age x in the alternative distribution
A
at the census date, Rx—1,0
is the same for age x-1, and
A
A
Dx,t and NMx,t are deaths and net migration for the interval
beginning at time t, respectively, consistent with the alterA
native population, age x at time t+1. Note that Rx,0 Rx,0
is
the inflation-deflation factor for age x. A similar adaptation
can be applied to equations (11) and (12) to obtain
expressions for estimates for age 0 and the open age
category, respectively.
The inflation-deflation procedure does not preserve exact
additivity of the age groups to the external sex-race-origin
total Rt. It is therefore necessary, in order to preserve the
balancing equation for the population totals, to introduce a
final proportional adjustment. This can be expressed by the
formula
Ⲑ
´
Rx,t ⫽ R
x,t
Rt
(14)
100
兺R´
y,t
y=0
D–13
which effectively ensures the additivity of the resident
population estimates Rx,t by age (x) to the external total Rt.
The alternative base population for the census date
used in the Bureau’s population estimates program, including the survey control projections, is known as the Demographic Analysis (DA) resident population. Originally devised
to measure underenumeration in the census, this population is developed, up to age 65, from an historical series of
births, adjusted for underregistration. This series is updated
to a population on April 1, 1990, by sex and race using
cumulative data on deaths and net migration from date of
birth to the census date. For ages 65 and over, the
population is based on medicare enrollees, as this population is considered more complete than the census enumeration with respect to coverage and age reporting. This
population is assumed to possess internal consistency with
respect to age superior to that of the census because births
and deaths, which form the mainstay of the estimates of
age categories, are based on administrative data series
rather than subjective reporting of age from an underenumerated population. Some adaptation of the administrative
series was necessary to standardize the registration universe for the time series of births and deaths, which
changed, as various states were added to the central
registration network. For a description of this method, see
Fay, Passel and Robinson (1988).
One might reasonably ask why the results of the PostEnumeration Survey (PES), used to adjust the CPS controls for underenumeration, were not adopted as the alternative population. The reason is that the PES age distribution
was based on application of adjustment factors to the
census population defined only for large age categories.
This method does not address census-based inconsistencies among single years of age within the categories;
moreover, it introduces serious new inconsistencies between
ages close to the edges of the categories. Hence, its use
as an alternative base for inflation-deflation would be
inappropriate. This fact does not impair the appropriateness of PES-based adjustment as a means of addressing
undercoverage in the completed survey controls, since the
results may be superior in their treatment of other dimensions of the population distribution, such as state of residence, race, origin, or large aggregates of age.
Having noted the relative appropriateness of the DA
base population for the inflated series, it is not without
technical limitations.
1. Because of its dependency on historical vital statistics
data, it can only be generated for three categories of
race—White, Black, and all other. The distribution of
the third category to American Indian and API assumes
the distribution of the census base for each age-sex
group. Therefore, spurious disturbances in the census
age distributions that differ for these two groups remain
uncorrected in the DA base population.
2. For the same reason, the two categories of Hispanic
origin cannot be generated directly in the DA base. The
practical solution was to assume that the proportion
Hispanic in each age, sex, and race category matched
the 1990 census base population. Because of this
assumption, no distinctly Hispanic properties of the
age discontinuity in the census population could be
reflected in the DA base employed in the estimates.
3. The reliance by the DA procedure on medicare enrollee
data for the population 65 years of age and over, while
defensible as a basis for adjusting the elderly population, leaves open the possibility of inconsistency in the
age distribution around age 65.
4. The DA procedure assumes internally consistent registration of deaths and an accounting of net migration
in the historical series, although adaptation had to be
made for the changing number of states reporting
registered births and deaths to the Federal government. Deviations from this assumption could affect the
internal consistency of the age distribution, especially
for the older ages under 65.
5. Because the DA population is based primarily on vital
registration data, race reporting does not always match
race reporting in the census. In particular, births and
deaths reported to NCHS are less likely to be coded
American Indian and Asian and Pacific Islander than
respondents in the census. While this has no direct
effect on estimated distributions of population by race
(inflation-deflation only affects age within sex-race
totals), the population defining the ‘‘ageable’’ attributes
of the base age distribution for these groups is smaller
(roughly 15 percent for most ages) than the base used
to compute sex-race-origin-specific population totals.
6. The inflation-deflation method, as currently implemented,
assumes current deaths and all the components of
migration to match in the DA universe and the census
base universe by sex, race, and origin. This amounts
A
A
and NMx,t
in equation (13) sum to D
to saying that Dx,t
and NM in equation (1), respectively. In the case of
births, we assume births adjusted for underregistration
to be consistent with the DA population, and births
without adjustment to be consistent with the censuslevel population. In principle, the application of components of change to a DA-consistent population in
equation (13) roughly parallels the derivation of the DA
population.6 However, the components used to update
the census-level population in equations (1) or (7)
6
One deviation from this rule occurs in the treatment of births by race.
The DA population defines race of child by race of father; the postcensal
update currently assigns race of child by race of mother. This practice will
likely be modified in the near future. A second deviation occurs in the
accounting of undocumented immigration and the emigration of legal
residents. As of 1995, the postcensal estimates had incorporated revised
assumptions, which had not been reflected in the DA base population;
moreover, the revised assumptions were conspicuously ‘‘census-level,’’
rather than ‘‘DA-level’’ assumptions.
D–14
should theoretically exclude the birth, death, or migration of persons who would not be enumerated, if the
census-level character of the estimates universe is to
be maintained.
Finally, we note the DA population can be estimated only
for the resident population universe, so the process of
estimating age distributions must be carried out on the
resident population. The accounting of total population, on
the other hand, is for the civilian noninstitutional universe.
The resident universe must then be adapted to the civilian
noninstitutional population by subtracting resident Armed
Forces and civilian institutional population estimates and
projections by age, sex, and race, from the resident
population, as of the estimate or projection reference date.
Fortunately, our sources of information for the institutional
and Armed Forces populations yield age distributions for
any date for which a total population is available; hence,
this adaptation is uncomplicated.
Distribution of Census Base and Components of
Change by Age, Sex, Race, and Hispanic Origin
Our discussion until now has addressed the method of
estimating population detail in the CPS control universe,
and has assumed the existence of various data inputs. The
current section is concerned with the origin of the inputs.
Modification of the census race and age distributions.
The distribution of sex, race, and Hispanic origin in the
census base population is determined by the census, but
with some adaptation. The 1990 census included questions
on sex, race, and Hispanic origin, as was the case in 1980.
The race question elicited responses of White, Black,
American Indian, Eskimo, Aleut, and ten Asian and Pacific
Islander categories, including ‘‘other API.’’ It also allowed
respondents to write-in a race category for ‘‘other race.’’
The Hispanic origin question elicited responses of ‘‘No (not
Spanish/Hispanic),’’ and a series of ‘‘Yes’’ answers, including Mexican, Puerto Rican, Cuban, and other Spanish/Hispanic,
with the last category offering a possibility of write-in. While
the initial census edit process interpreted the write-in
responses to the race question, it left a substantial number
of responses not directly codable to any White, Black,
American Indian, Eskimo, Aleut, or Asian and Pacific
Islander group. In 1980 this ‘‘other or not specified’’ category comprised 6.7 million persons; in 1990, it comprised
9.8 million persons. The overwhelming majority, in both
cases, consisted of persons who gave a positive response
to the Hispanic origin question, and/or a Hispanic origin
response to the race question. However, the Hispanic
component of the ‘‘other or not specified’’ race category
comprised somewhat less than half of the total Hispanic
population (based on the Hispanic origin response), with
most of the balance classified as White.
The existence of a residual race category with these
characteristics was inconsistent with other data systems
essential to the estimation process, namely vital statistics
and Armed Forces strength statistics. It also stood at
variance with Office of Management and Budget Directive
15, a 1977 ruling intended to standardize race and ethnicity
reporting across Federal data systems. This directive specified an exhaustive distribution of race that could either be
cross-classified with Hispanic origin, into a 4-by-2 matrix,
or combined into a single-dimensional variable with five
categories, including American Indian, Eskimo, and Aleut
(one category), Asian and Pacific Islander (one category),
non-Hispanic White, non-Hispanic Black, and Hispanic
origin. It was thus necessary to adapt the census distribution in such a way as to eliminate the residual race
category. The Bureau opted to follow the 4-by-2 crossclassification in 1990. Prior to 1990, the American Indian
and Asian and Pacific Islander categories were combined
into a single category in all Census Bureau population
estimates, and the race distribution included persons of
Hispanic origin separately, with race unspecified. In 1990based estimates, these two racial categories were separated, and the procedure was expanded to estimate the full
race-Hispanic cross-classification, although at present this
disaggregation is recombined for the purpose of weighting
the CPS.
The modification of the 1990 race distribution occurred
at the level of individual microdata records. Persons of
unspecified race were assigned to a racial category using
a pool of ‘‘race donors.’’ This pool was derived from
persons with a specified race response, and the identical
response to the Hispanic origin question (e.g., Mexican,
Puerto Rican, Cuban, other Hispanic, or not Hispanic).
Thus, a respondent’s race (if initially unspecified) was, in
expectation, determined by the racial distribution of persons with the same Hispanic origin response who resided
in the vicinity of the respondent.
The distribution by age from the 1990 census also
required modification. The 1990 census form asked respondents to identify their ages and their years of birth. While
the form provided explicit direction to include only those
persons who were alive and in the household on the
census date, there was no parallel instruction to state the
ages of household members as age on April 1, 1990. It was
apparent that many respondents reported age at time of
completion of the form, time of interview by an enumerator,
or time of their next anticipated birthday. Any of these could
occur several months after the April 1 reference date. As a
result, age was biased upward, a fact most noticeable
through gross understatement of the under 1-year-of-age
category. While this distribution was published in most
1990 census publications and releases, it was considered
inadequate for postcensal estimates and survey controls.
The resulting modification was based on reported year of
birth. Age was respecified by year of birth, with allocation to
first quarter (persons aged 1990 minus year of birth) and
last three quarters (aged 1989 minus year of birth) based
on a historical series of birth by month derived from birth
registration data. This methodology is detailed in U.S.
Bureau of the Census (1991).
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As was the case with race, the recoding of age occurred
directly on the individual records, after preliminary edits to
eliminate reports of year of birth or age inconsistent with
other variables (such as household relationship). The
tabulations required for the base distribution for estimates
could thus be obtained by simple frequency distributions
from the individual-record file. There was also a trivial
modification of the distribution by sex that came about
because the assignment of a very small percentage of
census respondents of unknown sex was linked to respondent’s age. The modification of the age distribution will be
discussed under ‘‘Distribution of Population Projections by
Age.’’ The resulting distribution is known to the Census
Bureau estimates literature as ‘‘MARS,’’ an acronym for
‘‘modified age, race, and sex.’’
We note parenthetically that the Demographic Analysis
(DA) population, used as the alternative base for the
inflation-deflation method, did not require modifying with
respect to age and race because it was derived primarily
from vital statistics and immigration. However, the historical
distribution of international migrants by race was based on
the MARS distribution by race within country of birth. More
importantly, the need to expand the race detail of the
original DA population required introducing the MARS
distribution to the split between American Indians and API,
as well as any indirect effect of MARS on the distribution by
Hispanic origin within race.
Distribution of births and deaths by sex, race, and
Hispanic origin. The principal source of information on
sex and race for births and deaths is the coding of sex and
race on birth and death certificates (race of mother, in the
case of births). These results are coded by NCHS on detail
files of individual birth and death records. Hispanic origin is
also coded on most vital records, but a substantial number
of events did not receive a code, in some cases because
they occurred in states that do not code Hispanic origin on
birth and death certificates. For births, the unknown category was small enough to be distributed to Hispanic and
not Hispanic in proportion to the MARS distribution of
persons aged under 1 year. For deaths, the number of
unknowns was sufficiently large (in some years, exceeding
the number of deaths known to be Hispanic) to discourage
the use of the NCHS distribution. Life tables were applied
to a projected Hispanic origin population by age and sex;
the resulting distribution was aggregated to produce totals
for Hispanic and not Hispanic by sex. While the resulting
distributions of births and deaths resemble closely the
MARS distribution of the base population, a few discrepancies remained. Some are of sufficient concern to prompt
adaptations of the distributions.
1. NCHS final data through 1992 included, for both births
and deaths, a small category of ‘‘other races,’’ not
coded to one of the categories in the MARS (OMB)
distribution. The practice to date has been to include
these in the Asian and Pacific Islander category,
although a closer examination of the characteristics of
these events in 1994 suggested some should be
coded elsewhere in the distribution, principally Hispanic origin, with race other than API.
2. The reporting of American Indian and Asian and Pacific
Islander categories in the census, hence also in the
MARS base population, has tended to exceed the
reporting of these categories in birth and death data.
This fact is symptomized by unrealistically low fertility
and mortality rates when MARS-consistent population
estimates are used as denominators. This fact has not
been addressed in estimates and projections to date,
but is currently under review for possible future adaptation.
3. Persons of Hispanic origin and race other than White
are substantially more numerous in the MARS base
population than in the vital statistics universe. This can
best be explained by the procedure used to code race
in the base population. The initial edit of write-in
responses to the census race question was done
independently of the Hispanic origin response. Consequently, a considerable number of write-in responses
allocated to non-White racial categories were of Hispanic origin. While the subsequent allocation of the
‘‘other (unspecified) race’’ category in the MARS procedure did indeed reflect Hispanic origin responses, it
was possible for ‘‘race donors’’ selected for this procedure to have been persons of previously assigned
race. Because the census (MARS) base population
defines the population universe for estimates and
projections, the non-White race categories of the Hispanic origin population have been estimated using a
race-independent assumption for Hispanic age-specific
fertility and mortality rates, applied to MARS-consistent
projected populations. The NCHS race totals for Hispanic and non-Hispanic combined have been considered sufficiently close to consistency with MARS to
warrant their adoption for the population estimates;
hence, the non-Hispanic component of each racial
group is determined by subtraction of Hispanic from
the total. Hence, this adaptation will have no direct
effect on second stage controls to the CPS, as long as
the control procedure does not depend on the crossclassification of race with Hispanic origin.
A further issue that has not been fully addressed is the
possible effect on the race distribution of the practice of
identifying the race of a child by the race of its mother.
Research is currently in progress to define race and
Hispanic origin of child by a method that recognizes the
race and origin of both parents, using census results on the
reported race of children of biracial parentage.
Deaths by age. The distribution of deaths by age presents
yet another need for adaptation of the population universe.
However, its use for census-based estimates requires the
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application of projected rates to the oldest age groups,
rather than a simple decrement of the population by deaths
in an age group. Because a cohort-component method (or
in the present case, inflation-deflation) is required to estimate the age distribution of the population, the procedure
is extremely sensitive to the reporting of age on death
certificates and in the Demographic Analysis base population among elderly persons. We recall that medicare enrollees, not vital statistics, form the basis for the DA age
distribution of the elderly. Because deaths of the oldest age
groups may represent a substantial portion of the living
population at the beginning of an age interval, relatively
small biases caused by differential age reporting can
produce large cumulative biases in surviving populations
over a few years’ time. This, in fact, occurred for estimates
produced during the 1980s. It was the practice then to
decrement the population at all ages using data on numeric
deaths. Because there were not enough deaths to match
the extreme elderly population, the population in the extreme
elderly groups grew rapidly in the estimates. This was most
noted in the population over 100 years of age; while this is
of no direct concern to the CPS, which does not disaggregate age above 85, the situation provoked legitimate
concern that an overestimate of the population 85 and over
was also occurring, albeit of lesser proportion. At the
opposite extreme, the application of numeric deaths for the
oldest age groups could produce negative populations, if
the number of deaths in a cohort exceeded the living
population at the beginning of the interval.
This problem was solved in 1992 by developing a
schedule of age-specific death rates using life tables for
racial and Hispanic origin categories. When life table death
rates are applied to a population, the fact that proportions
of the living population in each age group must die each
year ensures the timely demise of the oldest birth cohorts.
Of course, the life tables are themselves reliant on the
assumed compatibility of the numerators and denominators of the death rates. However, the fact that the population estimates underlying the rates had incorporated earlier
death rates was sufficient to limit any cumulative error from
differential age reporting. The current practice is to incorporate numeric deaths from NCHS through 69, apply life
table-based death rates to ages 70 and over by single year
of age, then proportionately adjust deaths to ages 70 and
over to sum to NCHS-based totals by sex and race. Thus,
the only cumulative bias entering the series affecting the
extreme elderly would arise from differential reporting of
age by under or over 70 years, which is deemed to be of
minor concern.
International migration by age, sex, race, and Hispanic
origin. Legal international migration to the U.S. is based on
actual records of immigration from INS or refugee arrivals
from ORR, so the statistics include direct reporting of sex
and country of birth or citizenship as well as age. Race and
Hispanic origin are not reported. The procedure to obtain
the race and Hispanic origin variables depends on a
distribution from the 1990 census sample edited detail file
(1 in 6 households or ‘‘long form’’ sample) of foreign-born
persons arrived since the beginning of 1985, by sex, race
(MARS categories) Hispanic origin, and country of birth.
This distribution is used to distribute male and female
immigrants and refugees by race and Hispanic origin. This
procedure is very robust for most country-of-birth categories, since migrants from most foreign countries are heavily
concentrated in a single race-origin category (the notable
exception being Canada).
For the remaining components of international migration, indirect methods are required to produce the distribution by age, sex, race, and origin.
1. For annual emigration of legal residents (222,000
total), the foreign-born and native-born were distributed separately. For emigration of the foreign-born, the
age-sex distribution is a byproduct of the determination of the annual allowance of 195,000 persons.
Because emigration is computed as a residual of the
foreign-born from two censuses (with adjustment for
age-specific mortality), their age and sex distribution
can rest on the censuses themselves. This logic also
yields country of birth, so race and origin are imputed
in the same manner as for legal immigrants and
refugees, previously described. The 27,000 annual
allowance of native-born emigrants is assigned a
distribution by all characteristics matching the nativeborn population from the 1990 census.
2. For net undocumented migration, we rely primarily on
a distribution supplied by INS, giving the distribution by
age, sex, country of birth, and period of arrival, for
persons who legalized their residence under provisions of the Immigration Reform and Control Act
(IRCA). Age is back-dated from date of legalization to
date of arrival, since undocumented migrants are
assumed to be residents of the United States from the
time they arrive. An allowance is made, based on a life
table application, for persons who died before they
could be legalized. Once again, country of birth was
used to assign race and Hispanic origin based on data
from the 1990 census on the race and Hispanic origin
of foreign-born migrants by country of birth. This
assumption is subject to two foreseeable biases; (1) it
takes no account of undocumented migrants who
never legalized their status, and who may have different characteristics than those who legalized; and (2) it
takes no account of persons who entered and departed
prior to legalizing, who would (if nothing else) tend to
make the migration flow more youthful since such
persons are older upon departure than upon entry.
3. The distribution by age and sex of the net migration
flow from Puerto Rico arises from the method of
imputation. The method of cohort survival was used to
estimate the net migration out of Puerto Rico between
the 1980 and 1990 census dates. This method yields
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an age distribution as a byproduct. Race and Hispanic
origin were not asked in the Puerto Rican census
questionnaire and could not be considered in the
census survival method. The distribution within each
age-sex category is imputed from the one-way flow of
persons from Puerto Rico to the United States from
1985 to 1990, based on the 1990 census of the 50
states and the District of Columbia.
4. As previously noted, we assume the overall net migration of legal temporary residents other than refugees to
be constant and of a magnitude and distribution necessary to maintain a constant stock of legal temporary
residents in the United States. The consideration of
this component in the accounting system is of virtually
no importance to population totals, but is quite important to the age distribution. Were it not considered, the
effect would be to age temporary residents enumerated in the census through the distribution; whereas, in
reality they are more likely replaced by other temporary residents of similar age. This stock distribution of
488,000 persons enumerated in the 1990 census is
derived by identifying characteristics, measured by the
census ‘‘long form’’ (20 percent sample) data, resembling those that qualify for various nonimmigrant visa
categories. The largest such category is foreign students. We estimate an annual distribution of net migration by computing the difference of two distributions of
population stock. The first is simply the distribution of
the 488,000 persons from the 1990 census. The
second is the distribution of the same population after
the effects of becoming 1 year older, including losses
to mortality, estimated from a life table. Thus, the
migration distribution effectively negates the effects of
cumulative aging of these persons in the cohortcomponent and inflation-deflation methods.
Migration of Armed Forces and civilian citizens by age,
sex, race, and Hispanic origin. Estimation of demographic detail for the remaining components of change
requires estimation of the detail of the civilian citizen
population residing overseas, as well as the Armed Forces
residing overseas and Armed Forces residing in the United
States. The first two are necessary to assign demographic
detail to the net migration of civilian citizens; the third is
required to assign detail to the effects of Armed Forces
recruitment on the civilian population.
Distributions of the Armed Forces by branch of service,
age, sex, race/origin, and location inside or outside the
United States are provided by the Department of Defense,
Defense Manpower Data Center. The location-and-servicespecific totals closely resemble those provided by the
individual branches of the services for all services except
the Navy. For the Navy, it is necessary to adapt the location
distribution of persons residing on board ships to conform
to census definitions, which is accomplished through a
special tabulation (also provided by Defense Manpower
Data Center) of persons assigned to sea duty. These are
prorated to overseas and U.S. residence, based on the
distribution of the total population afloat by physical location, supplied by the Navy.
In order to incorporate the resulting Armed Forces
distributions in estimates, the race-origin distribution must
also be adapted. The Armed Forces ‘‘race-ethnic’’ categories supplied by the Defense Manpower Data Center treat
race and Hispanic origin as a single variable, with the
Hispanic component of Black and White included under
‘‘Hispanic,’’ and the Hispanic components of American
Indian and API assumed to be nonexistent. There also
remains a residual ‘‘other race’’ category. The method of
converting this distribution to consistency with MARS employs,
for each age-sex category, the 1990 census MARS distribution of the total population (military and civilian) to supply
all MARS information missing in the Armed Forces raceethnic categories. Hispanic origin is thus prorated to White
and Black according to the total MARS population of each
age-sex category in 1990; American Indian and API are
similarly prorated to Hispanic and non-Hispanic. As a final
step, the small residual category is distributed as a simple
prorata of the resulting Armed Forces distribution, for each
age-sex group. Fortunately, this adaptation is very robust;
it requires imputing the distributions of only very small race
and origin categories.
The overseas population of Armed Forces dependents
is distributed by age and sex, based on an overseas
census conducted in connection with the 1970 census, of
U.S. military dependents. While this source obviously
makes no claim to currency, it reflects an age distribution
uniquely weighted in favor of young adult females and
children. Race and origin are based on the distribution of
overseas Armed Forces, with cross-categories of age-sex
with race-origin determined by the marginals. Detail for
civilian Federal employees overseas has been provided by
the Office of Personnel Management for decennial dates,
and assumed constant; their dependents are assumed to
have the same distribution as Armed Forces dependents.
Having determined these distributions for populations,
the imputation of migration of civilian citizens previously
described (equation (9)) can be carried out specifically for
all characteristics. The only variation occurs in the case of
age, where it is necessary to carry out the imputation by
birth cohort, rather than by age group.
The two very small components of deaths of the Armed
Forces overseas and net recruits to the Armed Forces from
the overseas population, previously described, are also
assigned the same level of demographic detail. Deaths of
the overseas Armed Forces are assigned the same demographic characteristics as the overseas Armed Forces. Net
recruits from overseas are assigned race and origin, based
on an aggregate of overseas censuses, in which Puerto
Rico dominates numerically. The age and sex distribution
follows the worldwide Armed Forces.
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The civilian institutional population by age, sex, race,
and origin. The fundamental source for the distribution of
the civilian institutional population by age, sex, race, and
origin is a 1990 census/MARS tabulation of institutional
population by age, sex, race, origin, and type of institution.
The last variable has four values: nursing home, correctional facility, juvenile facility, and a small residual. From
this table, participation rates are computed for the civilian
population. These rates are applied in each year to the
current estimated distribution of the civilian population. As
previously observed, the institutional population total is
updated annually, until July 1 of the year 2 years prior to the
CPS reference date, by the results of a special report on
selected group quarters facilities. The report provides an
empirical update of the group quarters populations by type,
including these four institutional types. The demographic
detail can then be proportionately adjusted to sum to the
type-specific totals. For dates beyond the last group quarters survey, the detailed participation rates are recomputed
for the last available date, and these are simply applied to
the estimated or projected civilian population distribution.
Population Controls for States
The second-stage weighting procedure for the CPS
requires a distribution of the national civilian noninstitutional population ages 16 and over by state. This distribution is determined by a linear extrapolation through two
population estimates for each state and the District of
Columbia. The reference dates are July 1 of the years that
are 2 years and 1 year prior to the reference date for the
CPS population controls. The extrapolated state distribution is forced to sum to the national total population ages 16
and over by proportional adjustment. For example, the
state distribution for June 1, 1996, was determined by
extrapolation along a straight line determined by two data
points (July 1, 1994, and July 1, 1995) for each state. The
resulting distribution for June 1, 1996, was then proportionately adjusted to the national total for ages 16 and over,
computed for the same date. This procedure does not allow
for any difference among states in the seasonality of
population change, since all seasonal variation is attributable to the proportional adjustment to the national population.
The procedure for state estimates (e.g., through July 1
of the year prior to the CPS control reference date), which
form the basis for the extrapolation, differs from the nationallevel procedure in a number of ways. The primary reason
for the differences is the importance of interstate migration
to state estimates and the need to impute it by indirect
means. Like the national procedure, the state-level procedure depends on fundamental demographic principles embodied in the balancing equation for population change; however, they are not applied to the total resident or civilian
noninstitutional population but to the household population
under 65 years of age. Estimates of the group quarters
population under 65 and the population 65 and over
employ a different logic, based (in both cases) on independent sources of data for population size, benchmarked to
the results of the last census. Furthermore, while nationallevel population estimates are produced as of the first of
each month, state estimates are produced at 1-year intervals, with July 1 reference dates.
The Population 65 Years of Age and Over
The base population by state (which can be either a
census enumeration or an earlier estimate) is divided into
three large categories; the population 65 years of age and
over, the group quarters population under 65, and the
household (nongroup quarters) population under 65. The
first two categories are estimated directly by stock-based
methodologies; that is, the population change is estimated
as the change in independent estimates of population for
the categories. In the case of the population ages 65 and
over, the principal data source is a serial account of
medicare enrollees, available by county of residence.
Because there is a direct incentive for eligible persons to
enroll in the medicare program, the coverage rate for this
source is very high. To adapt this estimate to 1990 censuslevel, the change in the medicare population from census
date to estimate date is added to the 1990 census enumeration of the population 65 and over.
The Group Quarters Population Under 65 Years of
Age
The group quarters population is estimated annually
from the same special report on selected group quarters
facilities, conducted in cooperation with the Federal and
State Cooperative Program for Population Estimates (FSCPE),
used to update national institutional population estimates.
Because this survey is conducted at the level of the actual
group quarters facility, it provides detail at any level of
geography. Its results are aggregated to seven group
quarters types: the four institutional types previously discussed, and three noninstitutional types: college dormitories, military barracks, and a small noninstitutional group
quarters residual. Each type is disaggregated to the population over and under 65 years of age, based on 1990
census distributions of age by type. The change in the
aggregate group quarters population under 65 forms the
basis for the update of the group quarters population from
the census to the estimate date.
The Household Population Under 65 Years of Age
The procedure for estimating the nongroup quarters
population under 65 can be analogized to the national-level
procedures for the total population, with the following major
differences.
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1. Because the population is restricted to persons under
65 years of age, the number of persons advancing to
age 65 must be subtracted, along with deaths of
persons under 65. This component is composed of the
64-year-old population at the beginning of each year,
and is measured by projecting census-based ratios of
the 64-year-old to the 65-year-old population by the
national-level change, along with proration of other
components of change to age 64.
2. All national-level components of population change
must be distributed to state-level geography, with
separation of each state total into the number of
events to persons under 65 and persons 65 and over.
Births and deaths are available from NCHS by state of
residence and age (for deaths). Similar data can be
obtained—often on a more timely basis—from FSCPE
state representatives. These data, after appropriate
review, are adopted in place of NCHS data for those
states. For legal immigration (including refugees), ZIP
Code of intended residence is coded by INS on the
immigrant public use file; this is converted to county
and state geography by a program known as ZIPCRS,
developed cooperatively by the Census Bureau and
the U.S. Postal Service (Sater, 1994). Net undocumented migration is distributed by state on the basis of
data from INS on the geographic distribution of undocumented residents legalizing their status under the
Immigration Reform and Control Act (IRCA). The emigration of legal residents is distributed on the basis of
the foreign-born population enumerated in the 1990
census.
3. The population updates include a component of change
for net internal migration. This is obtained through the
computation for each county of rates of out-migration
based on the number of exemptions under 65 years of
age claimed on year-to-year matched pairs of IRS tax
returns. Because the IRS codes tax returns by ZIP
Code, the previously mentioned ZIPCRS program is
used to identify county and state of residence on the
matched returns. The matching of returns allows the
identification for any pair of states or counties of the
number of ‘‘stayers’’ who remain within the state or
county of origin and the number of ‘‘movers’’ who
change address between the two. This identification is
based on the number of exemptions on the returns and
the existence or nonexistence of a change of address.
Dividing the number of movers for each pair by the
sum of movers and stayers for the state of origin yields
a matrix of out-migration rates from each state to each
of the remaining states. The validity of these interstate
migration rates depends positively on the level of
coverage (the proportion of the population included on
two consecutive returns). It is negatively affected by
the difference between tax filers and tax nonfilers with
respect to migration behavior, since nonfilers are assumed
to migrate at the same rate as filers.
The Exclusion of the Population Under 16 Years of
Age, Armed Forces, and Inmates of Civilian
Institutions
The next step in the state-level process is the estimation
of the age-sex distribution, which allows exclusion of the
population under 16. The major input to this procedure is a
nationwide inquiry to state governments and some private
and parochial school authorities for data on school enrollment. The number of school-aged children (exact age 6.5
to 14.5) for each annual estimate date is projected, without
migration, from the census date by the method of cohort
survival. This method begins with the number of persons
enumerated in the last census destined to be of school age
on the estimate date, known as the school-age cohort. For
reference dates more than 6.5 years from the census, the
enumerated cohort must be augmented with a count of
registered births occurring between the census date and
the estimate date minus 6.5 years. For example, if the
estimate reference date is July 1, 1995, the enumerated
cohort will consist of persons age 1.25 to 9.25, since the
census of April 1, 1990, is 5.25 years prior to the estimate
date. On the other hand, if the estimate reference date is
July 1, 1998, the cohort will consist of the census population aged under 6.25 years, plus registered births occurring
from April 1, 1990, through December 31, 1991. The
number of deaths of this cohort of children from the census
date to the estimate date (estimated from vital registration
data) is then subtracted to yield a projection of the schoolage population on the estimate reference date.
Subtracting the resulting projection from actual estimates of school-age population, determined by updating
the census school-age population via school enrollments,
yields estimates of cumulative net migration rates for
school-age cohorts for each state. These estimates are
used to estimate migration rates for each age group up to
age 18, using historical observation of the relationship of
the migration of each age group to the migration of the
school-age population. Populations by age are proportionately adjusted to sum to the national total, yielding estimates of the population under age 16. These are subtracted from the total to yield the population ages 16 and
over, required to produce the population of the CPS control
universe.
Once the resident population 16 years of age and older
has been estimated for each state, the population must be
restricted to the civilian noninstitutional universe. Armed
Forces residing in each state are excluded, based on
reports of location of duty assignment from the branches of
the Armed Forces (including the homeport and projected
deployment status of ships, for the Navy). The group
quarters report is used to exclude the civilian institutional
population. The group quarters data and some of the
Armed Forces data (especially information on deployment
of naval vessels) must be projected from the last available
date to the later annual estimate dates.
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Dormitory Adjustment
The final step in the production of the CPS base series
for states is the adjustment of the universe from the census
universe to the CPS control universe with respect to the
geographic location of college students living in dormitories. The decennial census form specifically directs students living in dormitories to report the address of their
dormitories, while CPS interviews identify the address of
the family home. A dormitory adjustment for each state is
defined as the number of students with family home
address in the state residing in dormitories (in any state)
minus the number of students residing in dormitories in the
state. The latter estimate is a product of the previously
mentioned group quarters report, restricted to college
dormitories. The former is the same group quarters dormitory estimate, but adjusted by a ratio of students by family
resident state to students by college enrollment state,
which is computed from data from the National Center for
Education Statistics (NCES). Adding the dormitory adjustment (which may be negative or positive) to the censuslevel civilian noninstitutional population ages 16 and over
yields the population ages 16 and over for a universe
consistent with the CPS in every regard except adjustment
for underenumeration.
ADJUSTMENT OF CPS CONTROLS FOR NET
UNDERENUMERATION IN THE 1990 CENSUS
Beginning in 1995, the CPS controls were adjusted for
net undercoverage in the 1990 census. This adjustment
was based on the results of the Post-Enumeration Survey
(PES) carried out in the months following the census. From
this, census coverage ratios were obtained for various
large ‘‘poststrata’’ or cross-categories of a few variables
determined to be most related to underenumeration. The
present section addresses briefly the methodology of this
survey, the application of coverage ratios for poststrata to
obtain numerical levels of underenumeration in the CPS
universe, and the application of the resulting numerical
adjustment to the CPS population controls.
Important to the rationale behind the adjustment of CPS
controls is the origin of the decision to adjust them for
underenumeration, given that population estimates published by the Census Bureau are not. The Post-Enumeration
Survey was, in its design, conceived as a method of
providing an adjustment for net underenumeration in the
1990 census. In the year following the 1990 census date,
estimates of net underenumeration were produced. In a
decision effective July 15, 1991, Secretary Robert Mosbacher announced a decision not to adjust the census for
undercount or overcount (Federal Register, 1991). Cited in
the decision was a large amount of research pointing to
improvement in the estimates of the national population
resulting from incorporation of the Post-Enumeration Survey, coupled with a lessening of the accuracy of estimates
for some states and metropolitan areas. The decision also
called for further research into the possibility of incorporating PES results in the population estimates programs.
Research conducted over the following 18 months under
the auspices of the Committee on Adjustment of Postcensal Estimates (CAPE), a committee appointed by the
Director of the Census Bureau, provided a description of
the PES methodology, and a detailed analysis of the effect
of adjustment on national, state, and county estimates.
This research is summarized in a report, which forms the
basis for all technical information regarding the PES discussed here (U.S. Census Bureau, 1992). After extensive
public hearings, including testimony by members of Congress, Barbara Bryant, then Director of the Census Bureau,
decided, effective December 30, 1992, not to incorporate
PES results in the population estimates programs, but
offered to calibrate Federally sponsored surveys, conducted by the Census Bureau, to adjusted population data
(Federal Register, 1993). The decision cited the finding by
CAPE that estimates of large geographic aggregates were
improved by inclusion of PES estimates, while the same
finding could not be determined for some states and
substate areas. In the course of 1993, the Bureau of Labor
Statistics opted to include the 1990 census, with adjustment based on the Post-Enumeration Survey, in the controls for the CPS, beginning January 1994.
We stress that the intent of the PES from its inception
was to serve as a device for adjusting the census, not for
the calibration of surveys or population estimates. Its
application to population controls for the CPS and other
surveys was based on an analysis of its results. Essential
to its usefulness is the primacy of national-level detail in the
evaluation of survey controls, coupled with the apparent
superior performance of the PES for large geographic
aggregates.
THE POST-ENUMERATION SURVEY AND
DUAL-SYSTEM ESTIMATION
The PES consisted of a reinterview, after the 1990
census, of all housing units within each of a sample of
small geographic units. The geographic unit chosen was
the census block, a small polygon of land surrounded by
visible features, frequently four-sided city blocks bounded
by streets. The sampling universe of all such blocks was
divided into 101 strata, based on certain variables seen to
be related to the propensity to underenumeration. These
consisted of geography, city size, racial and ethnic composition, and tenure of housing units (owned versus rented).
The strata were sampled; the entire sample consisted of
more than 5,000 blocks.
The reinterview and matching procedures consisted of
an interview with all households within each of the sampled
blocks followed by a comparison of the resulting PES
observations with original census enumeration records of
individual persons. Care was taken to ensure the independence of the reinterview process from the original enumeration, through use of different enumerators, as well as
D–21
independent local administration of the activity. Through
the comparison of census and PES records of individual
persons, matches were identified, as were nonmatches
(persons enumerated in one survey but not in the other),
and erroneous enumerations (persons who either should
not have been enumerated, or should not have been
enumerated in that block). Where necessary, follow-up
interviews were conducted of census and PES households
to determine the status of each individual with respect to
his/her match between census and PES or his/her possible
erroneous enumeration.
This matching procedure yielded a determination for
each block of the number of persons correctly enumerated
by the census only, the number of persons enumerated by
the PES only, and the number enumerated by both. The
number of persons missed by both census and survey
could then be imputed by observing the relationship—within
the PES sample—of the number missed by the census to
the number enumerated, and generalizing this relationship
to those missed by the PES sample. The resulting estimate
was called a ‘‘dual-system estimate,’’ because of its reliance on two independent enumeration systems. In most
cases, the dual-system estimate was higher than the
original enumeration, implying a net positive undercount in
the census. For some blocks, the dual-system estimate
was lower than the census count, meaning the number of
incorrect enumerations exceeded the number of persons
imputed by the matching process, resulting in a net overcount (negative undercount).
Having established dual-system estimates of the sampled
blocks, a new stratification—-this time of individual persons
(rather than blocks)—was defined; the resulting strata were
dubbed ‘‘poststrata,’’ as they were used for retrospective
analysis of the survey results (as opposed to being part of
the sampling design). These poststrata formed the basic
unit for which census coverage ratios were computed.
They were defined on data for characteristics considered
relevant to the likelihood of not being enumerated in the
census, including large age group, sex, race (including
residence on a reservation, if American Indian), Hispanic
origin, housing tenure (living in an owned versus rented
dwelling), large city, urban, or rural residence, and geographic region of residence. The choice of categories of
these variables was intended to balance two conflicting
objectives. The first was to define strata that were homogeneous with respect to the likelihood of enumeration in
the census. The second was to ensure that each stratum
was large enough to avoid spuriousness in the estimation
of census coverage. As a result, the categories were
asymmetric with respect to most of the variables: the
choice of categories of one variable depended on the value
of another. Seven categories of age and sex (four age
categories by two sex categories, but with males and
females combined for the youngest category) were crossed
with 51 categories of the other variables to produce a total
of 357 poststrata.
The percentages of undercount or overcount were computed for each poststratum by dividing the difference
between the dual-system estimate of population and the
census enumeration for the sampled population by the
census enumeration. Applying these ratios to the poststrata, this time defined for the entire noninstitutional
population of the United States enumerated in the 1990
census, yielded estimates of undercoverage. Among variables tabulated in the CPS, there was considerable variation in the adjustment factors by race, since race was an
important source of heterogeneity among poststrata. Moreover, as a result of the small number of age categories
distinguished in the poststrata, adjustment factors tended
to vary sharply between single year age groups close to the
limits of neighboring age categories.
Application of Coverage Ratios to the MARS
Population
Undercount ratios were applied to the 1990 MARS
distribution by age, sex, race, and origin. Because the
poststrata included, in their definition, one racial category
(‘‘Other’’) that did not exist in the MARS distribution, undercount ratios for census race-origin categories were imputed
to the corresponding MARS categories. These undercount
ratios were implemented in the MARS microdata file, which
was then tabulated to produce resident population by age,
sex, race, Hispanic origin, and state. ‘‘Difference matrices’’
were obtained by subtracting the unadjusted MARS from
the adjusted MARS distribution. This was done for both the
national age-sex-race-origin distribution, and the 51-state
distribution of the civilian noninstitutional population ages
16 years and over.
An important assumption in the definition of the difference matrix was the equality of the adjustment for resident
population and civilian noninstitutional population. This
amounted to an assumption of zero adjustment difference
for the active-duty military and civilian institutional populations. The assumption was natural in the case of the
institutional population, because this population was expressly
excluded from the PES. In the case of the Armed Forces,
the population stock is estimated, for both the nation and
for states, from data external to the census; hence, there
was no basis on which to estimate PES-consistent undercount for the Armed Forces population.
Once derived, the differences have been added to all
distributions of population used for independent controls
for the CPS produced since January 1, 1995. While it
would have been technically more satisfying to apply
undercount ratios, as defined by the PES, directly to the
CPS population controls, this would have required projections of the poststrata for CPS control reference dates, for
which no methodology has been developed.
This adjustment of the CPS universe is the final step in
the process of computing independent population controls
for the second-stage weighting of the Current Population
Survey.
D–22
A question that frequently arises regarding adjustment
for underenumeration is how it relates to inflation-deflation,
as previously discussed. We need to inflate estimated
populations to an ‘‘adjusted level’’ in order to apply cohortcomponent logic to the estimation of the age distribution.
Why then is it necessary to deflate to census level, then
inflate again to adjust for underenumeration? The answer
to this lies in the relationship of the purpose of the
inflation-deflation method to the method of adjustment. In
the case of inflation-deflation, the sole objective is to
provide an age distribution that is unaffected by differential
undercount and age reporting bias, from single year to
single year of age throughout the distribution. This is
necessary because cohort component logic advances the
population 1 year of age for each 1-year time interval. The
Demographic Analysis population meets this objective well,
as it is developed, independently of the census, from series
of vital and migratory events that are designed to be
continuous, based on time series of administrative data.
However, the definition of poststrata for dual-system estimates in the PES is based on the application of uniform
adjustment factors to large age categories, with large
differences at the limits of the categories. This would not
serve the needs of inflation-deflation, even though the PES
has been assumed to be a superior means of adjusting the
census for underenumeration (at least for subnational
geographic units).
THE MONTHLY AND ANNUAL REVISION
PROCESS FOR INDEPENDENT POPULATION
CONTROLS
Each month a projection of the civilian noninstitutional
population of the United States is produced for the CPS
control reference date. Each projection is derived from the
population at some base date and the change in the
population from the base date to the reference date of the
projection. In every month except January, the base date
(the date after which new data on population change can
be incorporated in the series) is 2 months prior to the CPS
reference date. In January, the entire monthly series back
to the last census is revised, meaning the base date is the
date of the last census (currently April 1, 1990). Early in the
decade (in the current decade, January 1, 1994), the
population from the most recent decennial census is introduced for the first time as a base for the January revision.
As a consequence of the policy of ongoing revision of a
month back, for monthly intervals from January 1 to
December 1, the monthly series of population figures
produced each month for 1 month prior to the CPS
reference date is internally consistent for the year of
reference dates from December 1 to November 1—meaning
the month-to-month change is determined by the measurement of population change. For actual CPS reference
dates from January 1 to December 1, the series is not
strictly consistent; there is a small amount of ‘‘slippage’’ in
the month-to-month intervals, which is the difference between:
1. The population change during the 1 month immediately preceding the CPS reference date, as measured
a month after the CPS control figure is produced, and
2. The population change in the month preceding the
CPS date, associated with the actual production of the
CPS controls.
This slippage is maintained for the purpose of preventing cumulative error, since the measurement of population
change in the last month at the time of measurement (2) is
based entirely on projection (no data are available to
measure the change); whereas, some key administrative
data (e.g., preliminary estimates of births and deaths) are
generally available for the month before the last (1). The
slippage rarely exceeds 20,000 persons for the population
ages 16 and over, compared to a monthly change in this
population that varies seasonally from roughly 150,000 to
260,000 persons, based on calendar year 1994. Because
the numerically largest source of slippage is usually the
projection of births, most of the slippage is usually confined
to the population under 1 year of age, hence, of no concern
to labor force applications of the survey.
Monthly population controls for states, by contrast,
normally contribute no slippage in the series from January
to December, because they are projections from July 1 of
the previous year. Generally, any slippage in the state
series is a consequence of their forced summation to the
national total for persons ages 16 and over. The annual
revision of the population estimates to restart the national
series each January affects states as well as the Nation, as
it involves not only revisions to the national-level estimates
of population change, but also the distribution of these
components to states, and an additional year of internal
migration data.
For the revision of the entire series each January,
preliminary estimates of the cumulative slippage for the
national population by demographic characteristic (not the
states) are produced in November and reviewed for possible effect on the population control series. Annual slippage (the spurious component of the difference between
December 1 and January 1 in CPS production figures) is
generally confined to less than 250,000 in a year, although
larger discrepancies can occur if the revision to the series
entails cumulative revision of an allowance for an unmeasured component (such as undocumented immigration),
the introduction of a new census, or a redefinition of the
universe (e.g., with respect to adjustment for undercoverage).
PROCEDURAL REVISIONS
The process of producing estimates and projections, like
any research activity, is in a constant state of flux. Various
technical issues are addressed with each cycle of estimates for possible implementation in the population estimating procedures, which carry over to the CPS controls.
These procedural revisions tend to be concentrated early
in the decade, because of the introduction of data from a
D–23
new decennial census, which can be associated with new
policies regarding population universe or estimating methods. However, they may occur at any time, depending
either on new information obtained regarding the components of population change, or the availability of resources
to complete methodological research.
REFERENCES
Ahmed, B. and Robinson, J. G. (1994), Estimates of
Emigration of the Foreign-Born Population: 1980-1990,
U.S. Census Bureau, Population Division Technical Working Paper No. 9, December, 1994.
Anderson, R. N., Kochanek, K. D., Murphy, S. L. (1997),
‘‘Report of Final Mortality Statistics, 1995,’’ Monthly Vital
Statistics Report, National Center for Health Statistics,
Vol. 45, No. 11, Supplement 2.
Fay, R. E., Passel, J. S., and Robinson, J. G. (1988),
The Coverage of the Population in the 1980 Census,
1980 Census of Population and Housing, U.S. Census
Bureau, Vol. PHC–E–4, Washington, DC: U.S. Government Printing Office.
Federal Register, Vol. 56, No. 140, Monday, July 22,
1991.
Federal Register, Vol, 58, No. 1, Monday, January 4,
1993.
Robinson, J. G. (1994), ‘‘Clarification and Documentation of Estimates of Emigration and Undocumented Immigration,’’ unpublished U.S. Census Bureau memorandum.
Sater, D. K. (1994), Geographic Coding of Administrative Records— Current Research in the ZIP/SECTORto-County Coding Process, U.S. Census Bureau, Population Division Technical Working Paper No. 7, June, 1994.
U.S. Census Bureau (1991), Age, Sex, Race, and
Hispanic Origin Information From the 1990 Census: A
Comparison of Census Results With Results Where
Age and Race Have Been Modified, 1990 Census of
Population and Housing, Publication CPH-L-74.
U.S. Census Bureau (1992), Assessment of Adjusted
Versus Unadjusted 1990 Census Base for Use in Intercensal Estimates: Report of the Committee on Adjustment of Postcensal Estimates, unpublished, August 7,
1992.
U.S. Immigration and Naturalization Service (1997),
Statistical Yearbook of the Immigration and Naturalization Service, 1996, Washington, DC: U.S. Government
Printing Office.
Ventura, S. J., Martin, J. A., Curtin, S. C., Mathews, T.J.
(1997a), ‘‘Report of Final Natality Statistics, 1995,’’ Monthly
Vital Statistics Report, National Center for Health Statistics, Vol. 45, No. 11, supplement.
Ventura, S. J., Peters, K. D., Martin, J. A., and Maurer, J.
D. (1997b), ‘‘Births and Deaths: United States, 1996,’’
Monthly Vital Statistics Report, National Center for Health
Statistics, Vol. 46, No. 1.
SUMMARY LIST OF SOURCES FOR CPS
POPULATION CONTROLS
The following is a summary list of agencies providing
data used to calculate independent population controls for
the CPS. Under each agency are listed the major data
inputs obtained.
1. The U.S. Census Bureau (Department of Commerce)
The 1990 census population by age, sex, race,
Hispanic origin, state of residence, and household or
type of group quarters residence
Sample data on distribution of the foreign-born
population by sex, race, Hispanic origin, and country of
birth
The population of April 1, 1990, estimated by the
Method of Demographic Analysis
Decennial census data for Puerto Rico
2. National Center for Health Statistics (Department of
Health and Human Services)
Live births by age of mother, sex, race, and Hispanic
origin
Deaths by age, sex, race, and Hispanic origin
3. The U.S. Department of Defense
Active-duty Armed Forces personnel by branch of
service
Personnel enrolled in various active-duty training
programs
Distribution of active-duty Armed Forces Personnel
by age, sex, race, Hispanic origin, and duty location
(by state and outside the United States)
Deaths to active-duty Armed Forces personnel
Dependents of Armed Forces personnel overseas
Births occurring in overseas military hospitals
4. The Immigration and Naturalization Service (Department of Justice)
Individual records of persons immigrating to the
United States, month and year of immigration, including age, sex, country of birth, and ZIP Code of intended
residence, and year of arrival (if different from year of
immigration)
5. Office of Refugee Resettlement (Department of Health
and Human Services)
Refugee arrivals by month, age, sex, and country of
citizenship
6. Department of State
Supplementary information on refugee arrivals
7. Office of Personnel Management
Civilian Federal employees overseas in overseas
assignments
E–1
Appendix E.
State Model-Based Labor Force Estimation
INTRODUCTION
Small samples in each state and the District of Columbia
result in unacceptably high variation in the monthly CPS
composite estimates of state employment and unemployment. The table below gives the sample sizes, the standard
errors, and coefficient of variation (CVs) for unemployment
and employment assuming an unemployment rate of 6
percent for the states and the Nation as a whole. These
numbers are based on the current design which was in
effect January 1996 through July 2001.1
Nation . . . . . . . . . . .
States . . . . . . . . . . .
The Signal Component
In the state labor force models, the signal component is
represented by an unobserved variance component model
with explanatory variables (Harvey, 1989)
Table E–1. Reliability of CPS Estimators Under the
Current Design
CV
STD
CV
Number
of households in
STD
sample
1.90
15.70
0.11
0.94
0.25
1.98
0.16
50,000
1.25 546-4055
Unemployment
rate
where ␪共t兲 represents the systematic part of the true labor
force value and e共t兲 is the sampling error. The basic
objective is to produce an estimator for ␪共t兲 that is less
variable than the CPS composite estimator. The signal
component, θ(t), represents the variation in the sample
values due to the stochastic behavior of the population.
The sampling error component, e(t), consists of variation
arising from sampling only a portion of the population.
Employment-topopulation
In an effort to produce less variable labor force estimates, BLS introduced time series models to ‘‘borrow
strength’’ over time. The models are based on a signalplus-noise approach to small area estimation where the
monthly CPS estimates are treated as stochastically varying labor force values obscured by sampling error (Tiller,
1992). Given a model for the true labor force values and
sampling error variance-covariance information, signalextraction techniques are used to estimate the true labor
force values. This approach was first suggested by Scott
and Smith (1974) and has been more fully developed by
Bell and Hillmer (1990), Binder and Dick (1990) and
Pfefferman (1992).
TIME SERIES COMPONENT MODELING
For each state, separate models for the CPS employmentto-population ratio and the unemployment rate are developed. In the signal-plus-noise model, the CPS labor force
estimate at time t, denoted by y(t), is represented as the
sum of two independent processes
y共t兲 ⫽ ␪共t兲 ⫹ e共t兲
(1)
1
They do not reflect the increased reliability after the sample expansion in July 2001 due to the State Children’s Health Insurance Program.
(See Appendix J.)
␪共t兲 ⫽ ␪ Ⲑ共t兲 ⫹ ␩共t兲
where
␪ Ⲑ共t兲 ⫽ X共t兲␤共t兲 ⫹ T共t兲 ⫹ S共t兲
(2)
X(t) = vector of known explanatory variables
β(t)= random coefficient vector
T(t) = trend component
S(t) = periodic or seasonal component
η(t)= residual noise.
The stochastic properties of each of these components are
determined by one or more normally distributed, mutually
independent white noise disturbances
␷j共t兲 ⬃ NID共0,␴␷2j兲
(3)
where j indexes the individual components.
Regression component. The regression component
consists of explanatory variables, X(t), with time-varying
coefficients, β(t)
M共t兲 ⫽ X共t兲␤共t兲.
(4)
This component accounts for variation in the CPS
estimates that can be explained by a set of observable
economic variables developed from auxiliary data sources
that are independent of the CPS sampling error. A common
core of state-specific explanatory variables has been developed: unemployment insurance claims from the FederalState Unemployment Insurance System (Blaustein, 1979),
E–2
a nonagricultural payroll employment estimate from the
BLS Current Employment Statistics (CES) program (US
DOL, 1997), and intercensal population estimates developed by the Census Bureau. The explanatory variable
included in the employment model is the CES estimate,
adjusted for strikes, expressed as a percent of the state’s
population. For the unemployment rate model the explanatory variable is the claims rate defined as the ratio of the
worker claims for unemployment insurance (UI) benefits to
CES employment.
The regression coefficients are modeled as a random
walk process
(5)
␤共t兲 ⫽ ␤共t ⫺ 1兲 ⫹ ␷␤共t兲, ␷␤共t兲 ⬃ NID共0,␴␤2 兲.
The effect of ␷␤共t兲 is to allow the coefficients to evolve
slowly over time. A zero variance, ␴␤2 ⫽ 0, results in a fixed
regression coefficient.
␷T共t兲 ⬃ NID共0,␴T2兲
(7)
␷R共t兲 ⬃ NID共0,␴R2兲
E 关␷TⲐ 共t兲␷T*共t兲兴 ⫽ 0.
The larger the variances, the greater the stochastic
movements in the trend. The effect of ␷T共t兲 is to allow the
level of the trend to shift up and down, while ␷R共t兲 allows the
slope to change. This two-parameter trend model allows for
a variety of patterns. If the variances of the two disturbances are both zero, then this component reduces to a
fixed linear trend. A random walk results when the level
variance is positive and the slope is identically zero and,
when explanatory variables are included in the model,
results in an intercept which varies over time.
Time series components. The explanatory variables in
the models account for an important part of the variation in
the CPS estimates but, because of definitional and other
differences, leave significant amounts of the nonsampling
error variation in the CPS estimates unexplained (Tiller,
1989). Therefore, stochastic trend and seasonal components are added to the model to adjust the explanatory
variables for differences from the CPS measure of state
employment and unemployment. Since the explanatory
variables account for a portion of the trend and seasonal
variation in the CPS, these stochastic time series components are residuals; they do not account for all the trend
and seasonal variation in the CPS.
Residual seasonal component. This component is the sum
of up to six trigonometric terms associated with the 12-month
frequency and its five harmonics
Residual trend component. The trend component is represented by a local approximation to a linear trend with a
random level, T共t兲, and slope, R(t)
2␲j
␻j = 12
T共t兲 ⫽ T共t⫺1兲 ⫹ R共t⫺1兲 ⫹ ␷⌻* 共t兲
and
R共t兲 ⫽ R共t⫺1兲 ⫹ ␷R共t兲
where
6
S共t兲 ⫽
兺 S 共t 兲.
j
(8)
j⫽1
Each frequency component is represented by a pair of
stochastic variables
Sj共t兲 ⫽ cos共␻j兲Sj共t⫺1兲 ⫹ sin共␻j兲Sj*共t⫺1兲 ⫹ ␷sj共t兲
(9)
Sj*共t兲 ⫽ ⫺sin共␻j兲Sj共t⫺1兲 ⫹ cos共␻j兲Sj*共t⫺1兲 ⫹ ␷*sj共t兲
where ␷sj and ␷*sj are zero mean white noise processes
which are uncorrelated with each other, and have a common variance ␴2s . The white noise disturbances are assumed to have a common variance, so that the change in
the seasonal pattern depends upon a single parameter. If
the common variance is zero then the seasonal pattern is
fixed over time. The expected values of the seasonal
effects add to zero over a 12-month period.
m
␷⌻*共t兲 ⫽
兺 ␦ ␰ 共t 兲 ⫹ ␷ 共t 兲
k k
Residual noise component. The residual noise component consists of random variation unaccounted for by other
components plus unusually large transitory fluctuations or
outliers
T
k⫽1
and
␰k共t兲 ⫽
兵
1 if t ⫽ tk
0 if t ⫽ tk.
(6)
External shocks which cause permanent shifts in the
level of the trend are specified in the disturbance term
associated with the trend level, ␷T共t兲. The coefficient, ␦k,
represents the impact of the shock at time tk.
The disturbance terms ␷T共t兲and ␷R共t兲 are assumed to be
mutually uncorrelated white noise random variates with
zero means and constant variances
␩共t兲 ⫽ ⌱共t兲 ⫹ O共t兲.
(10)
Irregular component, Ι(t). In some cases after estimating
the signal and sampling error components, we find that the
signal still contains significant irregular movements, not
attributable to sampling error, which tend to average to
zero over a short period of time. In this case an additional
irregular component is added to the model specification to
E–3
further smooth the estimates of the signal. This component
is specified as consisting of a single white noise disturbance with a zero mean and constant variance
⌱共t兲 ⫽ ␷⌱共t兲, ␷⌱共t兲 ⬃ N共0,␴⌱2兲.
(11)
If the variance for this component is zero, then the
irregular is identically zero and can be dropped from the
model.
Additive outlier component, O(t). An additive outlier represents a one-period transitory shift in the level of the
observed series
O共t兲 ⫽
兺 ␭ ␨ 共t 兲
j j
j
where
␨j共t兲 ⫽
兵
1 if t ⫽ j
0 otherwise.
(12)
The coefficient ␭j is the change in the level of the series at
time j. Time series are typically influenced by exogenous
disturbances that affect specific observations. These outliers may occur because an unusually nonrepresentative
sample of households was selected or because some real,
nonrepeatable event occurred in the population. Irrespective of its origin, an outlier of this type affects only a single
observation at a time and is unrelated to the time series
model of the signal. Because an outlier represents a
sudden, large, temporary change in the level of the CPS,
the model may attempt to initially adjust to this break as if
it were permanent. Accordingly, it is important to identify
such outliers and then discount them when estimating the
signal.
While the general model of the signal, just described, is
very flexible, not all of the components discussed above
are necessarily needed. The seasonal component may
have less than 6 frequency components, depending upon
the seasonal nature of the series being modeled. The
regressor variables may be able to explain a substantial
amount of variation in the observed series with fixed
coefficients. On average, the CES explains 11 percent of
the month-to-month variation in the CPS employment-topopulation ratio (CPSEP) while the UI data account for
about 15 percent of the monthly variation in the CPS
unemployment rate (CPSRT). If good regressor variables
are not available, the signal may be represented by just the
time series component; this results in a univariate analysis.
Sampling error component. Sampling error is defined as
the difference between the population value, θ(t) , and the
survey estimate
e共t兲 ⫽ y共t兲 ⫺ ␪共t兲
(13)
where e(t) has the following properties:2
E关e共t兲兴 ⫽ 0
(14)
Var关e共t兲兴 ⫽ ␴e2共t兲
(15)
␳e共l兲 ⫽
E兵e共t兲e共t⫺l兲其
␴e2共t兲
(16)
The CPS estimates contain measurement error which is
very difficult to quantify. We ignore this source of error by
treating our target variable as the set of values the CPS
would produce with a complete census of the population.
The sampling error is, therefore, assumed to have a zero
expectation. While the variances and, hence, the covariances change over time, the autocorrelations are treated
as stationary (see below.)
When modeling the sampling error, it is important to
account for sample design features which are likely to have
a major effect on the error structure e(t). The autocorrelation structure of the CPS sampling error depends strongly
upon the CPS rotating panel design and population characteristics (see Chapter 3). Another important characteristic of the state CPS estimator is its changing reliability over
time. That is, the absolute size of the sampling error is not
fixed but changes because of redesigns, sample size
changes, and variation in labor force levels. Prior to 1985,
some state estimates were produced from a national
design. Special sample supplementations under the old
national design also had an effect on the reliability of
selected state samples in the late 1970s and early 1980s.
A state-based design was phased in during 1984/85 along
with improved procedures for noninterviews, ratio adjustments, and compositing. This redesign had a major effect
on the reliability of many of the state CPS estimates. Even
with a fixed design and sample size, the variance of the
sampling error component changes because it is also a
function of the size of the labor force characteristics of the
population being measured. Since the CPS variance changes
across time and the autocorrelation structure is stable over
time, we express e(t) in multiplicative form as
(17)
e共t兲 ⫽ ␥共t兲e*共t兲.
This expression allows us to capture the autocorrelated
and heteroscedastic structure of e(t). The variance inflation
factor, ␥共t兲, accounts for heteroscedasticity in the CPS and
is defined by
␴e共t兲
␥共t兲 ⫽ ␴
e*
(18)
where ␴e共t兲 is the GVF estimate of the standard error for the
CPS estimate and ␴e* is the standard error of the autoregressive moving average (ARMA) process, e*共t兲.
2
In the context of time series analysis, we view the variances and
covariances of the CPS estimators as properties of the sampling error
component.
E–4
The CPS autocorrelation structure is captured through
e*共t兲 which is modeled as an ARMA process
⫺1
e*共t兲 ⫽ ␾ 共L兲␪共L兲␷e*共t兲
(19)
where L is the lag operator, that is, Lk共Xt兲 ⫽ Xt⫺k so that
␾共L兲 ⫽ 1 ⫺ ␾1L⫺␾2L2 ⫺ . . . ⫺␾pLp
and
␪共L兲 ⫽ 1 ⫺ ␪1L⫺␪2L2 ⫺ . . . ⫺␪qLq.
(20)
The
parameters, ␾1, ␾2 , . . ., ␾p, and ␪1, ␪2, . . ., ␪q are
estimated from the sampling error lag correlations (Dempster and Hwang, 1990) through the autocorrelation function
␳e*共l兲 ⫽
␪共L兲␪共L⫺1兲
␾共L兲␾共L⫺1兲
(21)
where we set ␳e*共 ⫺ l兲 ⫽ ␳e*共l兲. The coefficients θ and φ are
then used to compute the impulse response weights {gk}
through the generating function
g共L兲 ⫽ ␾⫺1共L兲␪共L兲.
(22)
The variance of the ARMA process e*共t兲 is computed as
⬁
2
␴e*
=
兺g .
2
k
(23)
DIAGNOSTIC TESTING
A model should adequately represent the main features
of movements in the CPS. An analysis of the model’s
prediction errors is the primary tool for assessing goodness
of fit. This is an indirect test of the model. The actual model
error is the difference between the true value of the signal
and the model’s estimate of that value. Since we do not
observe the true values, but only the CPS, which contains
sampling error, we cannot compute the actual model error.
The overall model, however, provides an estimate of the
signal and sampling error, which sum to an estimate of the
CPS. We may, therefore, use the model to predict new
CPS observations. If the model’s prediction errors are
larger than expected or do not average to zero, then this
suggests that the signal and/or noise components may be
misspecified. In this way, an examination of the model
errors in predicting the CPS provides an overall test of its
consistency with the CPS data.
The prediction errors are computed as the difference
between the current values of the CPS and the predictions
of the CPS made from the model, based on data prior to
the current period. Since these errors represent movements not explained by the model, they should not contain
any systematic information about the behavior of the signal
or noise component of the CPS. Specifically, the prediction
errors, when standardized, should approximate a randomly
distributed normal variate with zero mean (unbiased) and
constant variance. The models are subjected to a battery of
diagnostic tests to check the prediction errors for departure
from these properties.
k=0
We can then compute the variance inflation γ(t) factor
using CPS standard error estimates which are obtained
from generalized variance function (see Chapter 13) for
␴e共t兲.
ESTIMATION
The parameters of the noise component are derived
directly from design-based variance-covariance information. The state CPS variance estimates are obtained
through the method of generalized variance functions (see
Chapter 13). State level autocorrelations of the sampling
error are based on research conducted by Dempster and
Hwang (1990) that used a variance component model to
compute autocorrelations for the sampling error. After the
unobserved signal and noise components are put into the
state-space form, the unknown parameters of the variance
components of the signal are estimated by maximum
likelihood using the Kalman filter (Harvey, 1989). Given
these parameter values, the filter calculates the expected
value of the signal and the noise components at each point
of time conditional on the observed data up to the given
time point. As more data become available, previous
estimates are updated by a process called smoothing
(Maybeck, 1979). For more details, see Tiller (1989).
MONTHLY PROCESSING
State agency staff prepare their official monthly estimates using software developed by BLS that implements
the KF. The state model-based labor force estimates are
generally released 2 weeks after the release of the national
labor force estimates. The KF algorithm is particularly well
suited for the preparation of current estimates as they
become available each month. Since it is a recursive data
processing algorithm, it does not require all previous data
to be kept in storage and reprocessed every time a new
sample observation becomes available. All that is required
is an estimate of the state vector and its covariance matrix
for the previous month. The computer interface used by the
states to make, review, and transmit their model estimates
is a system of interactive programs called STARS (State
Time Series Analysis and Review System). The software is
interactive, querying users for their UI and CPS data and
then combining these data with CPS estimates to produce
model-based estimates.
END-OF-THE-YEAR PROCESSING
At the end of the year, a number of revisions are made
to the model estimates. The model estimates are first
re-estimated to incorporate new population estimates obtained
E–5
from the Census Bureau and revisions to state-supplied
data. The revised estimates are then smoothed and benchmarked to CPS annual averages. Finally, the benchmarked
series is then seasonally adjusted using the X-11 ARIMA
seasonal adjustment procedure.
Re-Estimation and Benchmarking
After the Census Bureau provides new population controls to BLS, the state CPS labor force estimates are
adjusted to these controls. Similarly, state agencies revise
their CES and UI claims data as more information about
the series becomes available throughout the year. Using
the revised state CPS and input data, the KF produces a
revised estimate for the current year. The revised estimates
are smoothed, using a fixed interval smoothing algorithm,
to incorporate data accumulated during the current year.
A benchmarking process follows the smoothing of the
forward filter estimates. Since the CPS state samples were
designed to produce reliable annual average estimates,
the annual average of the smoothed estimates is forced to
equal the CPS annual average. The method used during
the benchmarking procedure is called the Denton method.
It forces the average of the monthly smoothed estimates to
equal the CPS state annual averages while minimizing
distortions in the month-to-month changes of the smoothed
estimates.
Seasonal Adjustment
The model estimates are seasonally adjusted using the
X-11 ARIMA seasonal adjustment procedure. Seasonal
factors for the first half of the current year, January through
June, are based on the historical series. Factors for July
through December are calculated from a data series comprising the historical series and the forward filter estimates
for the first half of the current year. The models are
designed to suppress sampling error but not to decompose
the series into seasonal and nonseasonal variation. This
preprocessed series can then be adequately decomposed
by the X-11 filters (Tiller, 1996).
RESULTS
Using the basic model structure described above, employment and unemployment models were developed for all of
E–6
the states. Auxiliary variables, available by state, were
used in the regression components: Current Employment
Statistics survey employment (CESEM), worker claims for
unemployment insurance benefits (UI), and population
estimated by the Census Bureau. The general form for
each model is
CPSEP共t兲 ⫽ ␣共t兲CESEP共t兲 ⫹ Trend共t兲 ⫹ Seasonal共t兲 ⫹ Noise共t兲
(24)
CPSRT共t兲 ⫽ ␦共t兲CLRST共t兲 ⫹ Trend共t兲 ⫹ Seasonal共t兲 ⫹ Noise共t兲
where:
CESEP = 100(CESEM/POP)
CLRST = 100(UI/CESEM)
POP
= noninstitutional civilian 16+ population
The basic signal component consists of a regression
component with a time varying coefficient, trend level and
slope, and six seasonal frequencies, irregular variation and
outliers. The basic noise component consists of sampling
error. Once estimated, these models were subjected to
diagnostic testing. In a well-specified model, the standardized one-step-ahead prediction errors should behave approximately as white noise, that is, be uncorrelated with a zero
mean and fixed variance. To be acceptable, the final model
was required to show no serious departures from the white
noise properties. Once satisfactory results were obtained,
further decisions were based on goodness of fit measures
and Akaike’s Information Criterion (Harvey,1989) and on
subject matter knowledge.
Often, one or more of these components could be
simplified. For the signal, the trend slope could often be
dropped and the number of seasonal frequencies reduced.
In many cases, the estimated variance of the irregular was
close to zero, allowing it to be dropped from the model.
A graph of the CPS unemployment rate and the signal
from a state unemployment rate model is on page E–5. The
signal is considerably smoother than the CPS. Elimination
of the sampling error from the CPS by signal extraction
removed about 65 percent of the monthly variation in the
series.
REFERENCES
Bell, W.R. and Hillmer, S.C. (1990), ‘‘The Time Series
Approach to Estimation for Repeated Surveys,’’ Survey
Methodology, 16, 195-215.
Binder, D.A. and Dick, J.P. (1989),‘‘Modeling and Estimation for Repeated Surveys,’’ Survey Methodology, 15,
29-45.
Blaustein, S.J. (1979), paper prepared for the National
Commission on Employment and Unemployment Statistics.
Dempster, A.P., and Hwang, J.S. (1990), ‘‘A Sampling
Error Model of Statewide Labor Force Estimates From the
CPS,’’ Paper prepared for the U.S. Bureau of Labor
Statistics.
Evans, T., Tiller, R., and Zimmerman, T. (1993), ‘‘Time
Series Models for State Labor Force Series,’’ Proceedings
of the Survey Research Methods Section, American
Statistical Association.
Harvey, A.C. (1989), Forecasting, Structural Time
Series Models and the Kalman Filter, Cambridge: Cambridge University Press.
Maybeck, P.S. (1979), Stochastic Models, Estimation,
and Control (Vol. 2), Orlando: Academic Press.
Pfeffermann, D. (1992), ‘‘Estimation and Seasonal Adjustment of Population Means Using Data From Repeated
Surveys,’’ Journal of Business and Economic Statistics, 9, 163-175.
Scott, A.J., and Smith, T.M.F. (1974), ‘‘Analysis of Repeated
Surveys Using Time Series Methods,’’ Journal of the
American Statistical Association, 69, 674-678.
Scott, A.J., Smith, T.M.F., and Jones, R.G. (1977), ‘‘The
Application of Time Series Methods to the Analysis of
Repeated Surveys,’’ International Statistical Review, 45,
13- 28.
Thisted, R.A. (1988), Elements of Statistical Computing: Numerical Computation, New York: Chapman and
Hall.
Tiller, R. (1989), ‘‘A Kalman Filter Approach to Labor
Force Estimation Using Survey Data,’’ Proceedings of the
Survey Research Methods Section, American Statistical
Association, pp. 16-25.
Tiller, R. (1992), ‘‘An Application of Time Series Methods
to Labor Force Estimation Using CPS Data,’’ Journal Of
Official Statistics, 8, 149-166.
U.S. Department of Labor, Bureau of Labor Statistics
(1997), BLS Handbook of Methods, Bulletin 2490.
Zimmerman, T., Tiller, R., and Evans, T. (1994), ‘‘State
Unemployment Rate Time Series Models,’’ Proceedings
of the Survey Research Methods Section, American
Statistical Association.
F–1
Appendix F.
Organization and Training of the Data Collection Staff
INTRODUCTION
The data collection staff for all Census Bureau programs
is directed through 12 regional offices (ROs) and 3 telephone centers (computer assisted telephone interviewing CATI). The ROs collect data in two ways; CAPI (computer
assisted personal interviewing) and PAPI (paper-and-pencil
interviewing). The 12 ROs report to the Chief of the Field
Division whose headquarters is located in Washington, DC.
The three CATI facility managers report to the Chief of the
National Processing Center (NPC).
ORGANIZATION OF REGIONAL OFFICES/CATI
FACILITIES
The staffs of the ROs and CATI facilities carry out the
Census Bureau’s field data collection programs, both sample
surveys and censuses. Currently, the ROs supervise about
6,000 part-time and intermittent field representatives (FRs)
who work on continuing current programs and one-time
surveys. Approximately 2,100 of these FRs work on the
Current Population Survey (CPS). When a census is being
taken, the field staff increases greatly.
The location of the ROs and the boundaries of their
responsibilities are displayed in Figure F–1. RO areas were
originally defined to evenly distribute the office workloads
for all programs. Table F–1 shows the average number of
CPS units assigned for interview per month in each RO.
A regional director is in charge of each RO. Program
coordinators report to the director through an assistant
regional director. The CPS is the responsibility of the
demographic program coordinator who has one or two
CPS program supervisors on staff. The program supervisor
has a staff of one or two office clerks working essentially full
time. Most of the clerks are full-time civil servants who work
in the RO. The typical RO employs about 100 to 250 FRs
who are assigned to the CPS. Most FRs also work on other
surveys. The RO usually has 15 to 18 senior field representatives (SFRs) who act as team leaders to FRs. Each
team leader is assigned 6 to 10 FRs. The primary function
of the team leader is to assist the program supervisors with
training and supervising the field interviewing staff. In
addition, the SFRs conduct response follow-up with eligible
households. Like other FRs, the SFR is a part-time or
intermittent employee who works out of his or her home.
Despite the geographic dispersion of the sample areas,
there is a considerable amount of personal contact between
the supervisory staff and the FRs. This is accomplished
mainly through the training programs and various aspects
of the quality control program. For some of the outlying
PSUs, it is necessary to use the telephone and written
communication to keep in continual touch with all FRs.
With the introduction of CAPI, the ROs also communicate
with the FRs using e-mail. Assigning new functions, such
as team leaders, also improves communications between
the ROs and the interviewing staff. In addition to communications relating to the work content, there is a regular
system for reporting progress and costs.
The CATI centers are staffed with one facility manager
who directs the work of two to three supervisory survey
statisticians. Each supervisory survey statistician is in
charge of about 15 supervisors and between 100-200
interviewers.
A substantial portion of the budget for field activities is
allocated to monitoring and improving the quality of the
FRs’ work. This includes FRs group training, monthly home
studies, personal observation, and reinterview. Approximately 25 percent of the CPS budget (including travel for
training) was allocated to quality enhancement. The remaining 75 percent of the budget went to FR and SFR salaries,
all other travel, clerical work in the ROs, recruitment, and
the supervision of these activities.
TRAINING FIELD REPRESENTATIVES
Approximately 20 to 25 percent of the CPS FRs leave
the staff each year. As a result, the recruitment and training
of new FRs is a continuing task in each RO. To be selected
as a CPS FR, a candidate must pass the Field Employee
Selection Aid test on reading, arithmetic, and map reading.
The FR is required to live in the Primary Sampling Unit
(PSU) in which the work is to be performed and have a
residence telephone and in most situations, an automobile.
As a part-time or intermittent employee, the FR works 40
hours or less per week or month. In most cases, new FRs
are paid at the GS-3 level and are eligible for payment at
the GS-4 scale after 1 year of fully successful or better
work. FRs are paid mileage for the use of their own cars
while interviewing and for commuting to classroom training
sites. They also receive pay for completing their home
study training packages.
FIELD REPRESENTATIVE TRAINING
PROCEDURES
Initial training for new field representatives. Each FR,
when appointed, undergoes an initial training program prior
to starting his/her assignment. The initial training program
F–2
Figure F–1. Map
F–3
Table F–1. Average Monthly Workload by Regional Office: 2001
Regional office
Base workload
CATI workload
Total workload
Percent
Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Boston . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Philadelphia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Detroit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kansas City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Seattle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Charlotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Atlanta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dallas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Denver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66,576
8,914
2,887
5,915
5,006
4,430
6,566
5,147
5,310
4,853
4,094
9,681
3,773
5,640
615
361
786
286
525
450
529
366
376
320
815
212
72,216
9,529
3,248
6,701
5,292
4,955
7,016
5,676
5,677
5,230
4,414
10,495
3,984
100.00
13.20
4.50
9.28
7.33
6.86
9.71
7.86
7.86
7.24
6.11
14.53
5.52
consists of up to 20 hours of pre-classroom home study,
3.5 to 4.5 days of classroom training (dependent upon the
trainee’s interview experience) conducted by the program
supervisor or coordinator, and as a minimum, an on-the-job
field observation by the program supervisor or SFR during
the FRs first 2 days of interviewing. The classroom training
includes comprehensive instruction on the completion of
the survey using the laptop computer. In classroom training, special emphasis is placed on the labor force concepts
to ensure that the new FRs fully grasp these concepts
before conducting interviews. In addition, a large part of the
classroom training is devoted to practice interviews that
reinforce the correct interpretation and classification of the
respondents’ answers.
Each FR completes a home study exercise before the
second month’s assignment and, during the second month’s
interview assignment, is observed for at least 1 full day by
the program supervisor or the SFR who gives supplementary training, as needed. The FR also completes a home
study exercise and a final review test prior to the third
month’s assignment.
FIELD REPRESENTATIVE PERFORMANCE
GUIDELINES
Training for all field representatives. As part of each
monthly assignment, FRs are required to complete a home
study exercise which usually consists of questions concerning labor force concepts and survey coverage procedures.
Once a year, the FRs are gathered in groups of about 12 to
15 for 1 or 2 days of refresher training. These sessions are
usually conducted by program supervisors with the aid of
SFRs. These group sessions cover regular CPS and
supplemental survey procedures.
Training for interviewers at the CATI centers. Candidates selected to be CATI interviewers receive 3 days of
training on using the computer. This initial training does not
cover subject matter material. While in training, new CATI
interviewers are monitored for a minimum of 5 percent of
their time on the computer. This is compared to 2.5 percent
monitoring time for experienced staff. In addition, once a
CATI interviewer has been assigned to conduct interviews
on the CPS, she/he receives an additional 3 1/2 days of
classroom training.
Performance guidelines have been developed for CPS
CAPI FRs for response/nonresponse and production.
Response/nonresponse rate guidelines have been developed to ensure the quality of the data collected. Production
guidelines have been developed to assist in holding costs
within budget and to maintain an acceptable level of
efficiency in the program. Both sets of guidelines are
intended to help supervisors analyze activities of individual
FRs and to assist supervisors in identifying FRs who need
to improve performance.
Each CPS supervisor is responsible for developing each
employee to his/her fullest potential. Employee development can only be accomplished by providing meaningful
feedback on a continuous basis. By acknowledging strong
points and highlighting areas for improvement, the CPS
supervisor can monitor an employee’s progress and take
appropriate steps to improve weak areas.
FR performance is measured by a combination of the
following: response rates, production rates, supplement
response, reinterview results, observation results, submitting accurate payrolls on time, meeting deadlines, reporting
to team leaders, and attending training sessions.
The most useful tool to help supervisors’ evaluate the
FRs performance is the CPS 11-39 form. These reports are
generated monthly and are produced using data from the
ROSCO and CARMN systems.
This report provides the supervisor with information on:
Workload and number of interviews
Response rate and adjective rating
Noninterview results (counts)
Production rate, adjective rating, and mileage
Observation and reinterview results
Meeting transmission goals
Rate of personal visit telephone interviews
Supplement response rate
Refusal/Don’t Know counts and rates
Industry and Occupation (I&O) entries Jeffersonville could
not code.
F–4
EVALUATING FIELD REPRESENTATIVE
PERFORMANCE
Census Bureau headquarters, located in Suitland, Maryland provides guidelines to the ROs for developing performance standards for FRs response and production rates.
The ROs have the option of using the guidelines, modifying
them, or establishing a completely different set of standards for their FRs. If the RO establishes their own
standards, the RO must notify the FRs of the standards.
Maintaining high response rates is of primary importance to the Census Bureau. The response rate is defined
as the proportion of all sample households eligible for
interview that are actually interviewed. It is calculated by
dividing the total number of interviewed households by the
sum of interviewed households and the number of refusals,
those that are temporarily absent, noncontacts, and noninterviewed households for other reasons. (All of these
noninterviews are referred to as Type A noninterviews.)
Type A cases do not include vacant units, those that are
used for nonresidential purposes, or other addresses that
are not eligible for interview.
Production guidelines. The production guidelines used in
the CPS CAPI program are designed to measure the
efficiency of individual FRs and the RO field functions.
Efficiency is measured by total minutes per case which
includes interview time and travel time. It is calculated by
dividing total time reported on payroll documents by total
workload. The standard acceptable minutes per case rate
for FRs varies with the characteristics of the PSU. When
looking at an FRs production, a program supervisor must
consider extenuating circumstances, such as:
• Unusual weather conditions such as floods, hurricanes,
or blizzards.
• Extreme distances between sample units, or assignment
covers multiple PSUs.
• Large number of inherited or confirmed refusals.
• Working part of another FRs assignment.
• Inordinate number of temporarily absent cases.
• High percentage of Type B/C noninterviews that decrease
the base or nonresponse rate.
• Other substantial changes in normal assignment conditions.
Supplement response rate. The supplement response
rate is another measure that CPS program supervisors
must use in measuring the performance of their FR staff.
Transmittal rates. The ROSCO system allows the supervisor to monitor transmittal rates of each CPS FR. A daily
receipts report is printed each day showing the progress of
each case on CPS.
Observation of field work. Field observation is one of the
methods used by the supervisor to check and improve
performance of the FR staff. It provides a uniform method
for assessing the FRs attitudes toward the job, use of the
computer and evaluating the extent to which FRs apply
CPS concepts and procedures during actual work situations. There are three types of observations:
1. Initial observations.
2. General performance review.
3. Special needs.
Initial observations are an extension of the initial classroom
training for new hires and provides on-the-job training for
FRs new to the survey. They also allow the survey supervisor to assess the extent to which a new CPS CAPI FR
grasps the concepts covered in initial training and, therefore, are an integral part of the initial training given to all
FRs. A 2-day initial observation (N1) is scheduled during
the FRs first CPS CAPI assignment. A second 1-day initial
observation (N2) is scheduled during the FRs second CPS
CAPI assignment. A third 1-day initial observation (N3) is
scheduled during the FRs fourth through sixth CPS CAPI
assignment.
General performance review observations are conducted
at least annually and allow the supervisor to provide
continuing developmental feedback to all CPS CAPI FRs.
Each CPS CAPI FR is regularly observed at least once a
year.
Special-needs observations are made when there is
evidence of a FR having problems or poor performance.
The need for a special-needs observation is usually detected
by other checks on the FR’s work. For example, specialneeds observations are conducted if a FR has a high Type
A noninterview rate, a high minutes per case rate, a failure
on reinterview, an unsatisfactory on a previous observation, a request for help, or for other reasons related to the
FR’s performance.
An observer accompanies the FR for a minimum of 6
hours during an actual work assignment. The observer
takes note of the FR’s performance including how the
interview is conducted and how the computer is used. The
observer stresses good interviewing techniques: asking
questions as worded and in the order presented on the
CAPI screen, adhering to instructions on the instrument
and in the manuals, knowing how to probe, recording
answers correctly and in adequate detail, developing and
maintaining good rapport with the respondent conducive to
an exchange of information, avoiding questions or probes
that suggest a desired answer to the respondent, and
determining the most appropriate time and place for the
interview.
The observer reviews the FR’s household performance
and discusses the FR’s strong and weak points with an
emphasis on correcting habits that interfere with the collection of reliable statistics. In addition, the FR is encouraged to ask the observer to clarify survey procedures not
F–5
fully understood and to seek the observer’s advice on
solving other problems encountered.
Unsatisfactory performance. When the performance of a
FR is at the unsatisfactory level over any period (usually 90
days), he/she may be placed in a trial period for 30 to 90
days. Depending on the circumstances, the FR will be
issued a letter stating that he/she is being placed in a
Performance Opportunity Period (POP) or a Performance
Improvement Period (PIP). These administrative actions
warn the FR that his/her work is substandard, makes
specific suggestions on ways to improve performance,
alerts the FR to actions that will be taken by the survey
supervisor to assist the FR to improve his/her performance,
and notifies the FR that he/she is subject to separation if
the work does not show improvement in the allotted time.
G–1
Appendix G.
Reinterview: Design and Methodology
INTRODUCTION
A continuing program of reinterviews on subsamples of
Current Population Survey (CPS) households is carried out
every month. Reinterview involves a second interview
where all the labor force questions are repeated. The
reinterview program is one of our major tools for limiting the
occurrence of nonsampling error and is a critical part of the
CPS program. The CPS reinterview program has been in
place since 1954. Reinterviewing for CPS serves two main
purposes: as a quality control (QC) tool to monitor the work
of the field representatives (FRs) and to evaluate data
quality via the measurement of response error (RE).
Prior to the automation of CPS in January 1994, the
reinterview consisted of one sample selected in two stages.
The FRs were primary sampling units and the households
within the FRs assignments were secondary sampling
units. In 75 percent of the reinterview sample differences
between original and reinterview responses were reconciled, and the results were used both to monitor the FRs
and to estimate response bias; that is, the accuracy of the
original survey responses. In the remaining 25 percent of
the sample the differences were not reconciled and were
used only to estimate simple response variance; that is, the
consistency in response between the original interview and
reinterview. Because the one sample approach did not
provide a monthly reinterview sample that was fully representative of the original survey sample for estimating
response error, the decision was made to separate the RE
and QC reinterview samples beginning with the introduction of the automated system in January 1994.
As a QC tool, reinterviewing is used to deter and detect
falsification. As such, it provides a means of limiting
nonsampling error as described in Chapter 15. The RE
reinterview currently measures simple response variance
or reliability.
The measurement of simple response variance in the
reinterview assumes an independent replication of the
interview. However, this assumption does not always hold,
since the respondent may remember his or her interview
response and repeat it in the reinterview (conditioning).
Also the fact that the reinterview is done by telephone for
personal visit interviews may violate this assumption. In
terms of the measurement of RE, errors or variations in
response may affect the accuracy and reliability of the
results of the survey. Responses from interviews and
reinterviews are compared and differences identified and
analyzed. (See Chapter 16 for a more detailed description
of response variance.)
Sample cases for QC and RE reinterviews are selected
by different methods and have somewhat different field
procedures. To minimize response burden, a household is
only reinterviewed one time (or not at all) during its life in
sample. This rule applies to both the RE and QC samples.
Any household contacted for reinterview is ineligible for
selection during its remaining months in sample. An analysis of respondent participation in later months of the CPS
showed that the reinterview had no significant effect on the
respondent’s willingness to respond (Bushery, Dewey, and
Weller, 1995). Sample selection for reinterview is done
immediately after the monthly assignments are certified
(see Chapter 4).
RESPONSE ERROR SAMPLE
The regular RE sample is selected first. It is a systematic
random sample across all households eligible for interview
each month. It includes households assigned to both the
telephone centers and the regional offices. Only households which can be reached by telephone and for which a
completed or partial interview is obtained are eligible for
RE reinterview. This restriction introduces a small bias into
the RE reinterview results because households without
‘‘good’’ telephone numbers are made ineligible for the RE
reinterview. About 1 percent of CPS households are assigned
for RE reinterview each month.
QUALITY CONTROL SAMPLE
The QC sample is selected next. The QC sample has
the FRs in the field as its first stage of selection. The QC
sample does not include interviewers at the telephone
centers. It is felt that the monitoring operation at the
telephone centers sufficiently serves the QC purpose. The
QC sample uses a 15-month cycle. The FRs are randomly
assigned to 15 different groups. Both the frequency of
selection and the number of households within assignments are based upon the length of tenure of the FRs.
Through a falsification study it was determined that a
relationship exists between tenure and both frequency of
falsification and the percentage of assignment falsified
(Waite, 1993 and 1997). Experienced FRs (those with at
least 5 years of service) were found less likely to falsify.
Also, experienced FRs who did falsify were more circumspect, falsifying fewer cases within their assignments than
inexperienced FRs (those with under 5 years of service).
G–2
Because inexperienced FRs are more likely to falsify,
more of them are selected for reinterview each month:
three groups of inexperienced FRs to two groups of
experienced FRs. On the other hand, since inexperienced
FRs falsify a greater percentage of cases within their
assignments, fewer of their cases are needed to detect
falsification. For inexperienced FRs, five households are
selected for reinterview. For experienced FRs, eight households are selected. The selection system is set up so that
an FR is in reinterview at least once, but no more than four
times within a 15-month cycle.
A sample of households assigned to the telephone
centers is selected for QC reinterview, but these households become eligible for reinterview only if recycled to the
field and assigned to FRs already selected for reinterview.
Recycled cases are included in reinterview because recycles
are more difficult to interview and may be more subject to
falsification.
All cases, except noninterviews for occupied households, are eligible for QC reinterview: completed and partial
interviews and all other types of noninterviews (vacant,
demolished, etc.). FRs are evaluated on their rate of
noninterviews for occupied households. Therefore, FRs
have no incentive to misclassify a case as a noninterview
for an occupied household.
Approximately 2 percent of CPS households are assigned
for QC reinterview each month.
REINTERVIEW PROCEDURES
QC reinterviews are conducted out of the regional
offices by telephone, if possible, but in some cases by
personal visit.1 They are conducted on a flow basis extending through the week following the interview week. They
are conducted mostly by senior FRs and sometimes by
program supervisors. For QC reinterviews, the reinterviewers are instructed to try to reinterview the original household respondent, but are allowed to conduct the reinterview
with another eligible household respondent.
The QC reinterviews are computer assisted and are a
brief check to verify the original interview outcome. The
reinterview asks questions to determine if the FR conducted the original interview and followed interviewing
procedures. The labor force questions are not asked.2 The
QC reinterview instrument also has a special set of outcome codes to indicate whether or not any noninterview
misclassifications occurred or any falsification is suspected.
RE reinterviews are conducted only by telephone from
both the telephone centers and out of the regional offices
(ROs). Cases interviewed at the telephone centers are
1
Because the mail component was ineffective in detecting falsification,
it was stopped as of March 1998.
2
Prior to October 2001, QC reinterviews asked the full set of labor
force questions for all eligible household members.
reinterviewed from the telephone centers, and cases interviewed in the field are reinterviewed in the field. At the
telephone centers reinterviews are conducted by the telephone center interviewing staff on a flow basis during
interview week. In the field they are conducted on a flow
basis and by the same staff as the QC reinterviews. The
reinterviewers are instructed to try to reinterview the original household respondent, but are allowed to conduct the
reinterview with another knowledgeable adult household
member.3 All RE reinterviews are computer assisted and
consist of the full set of labor force questions for all eligible
household members. The RE reinterview instrument dependently verifies household membership and asks the industry and occupation questions exactly as they are asked in
the original interview.4 Currently, no reconciliation is conducted.5
SUMMARY
Periodic reports on the QC reinterview program are
issued showing the number of FRs determined to have
falsified data. Since FRs are made aware of the QC
reinterview program and are also informed of the results
following reinterview, falsification is not a major problem in
the CPS. Only about 0.5 percent of CPS FRs are found to
falsify data. These FRs either resign or are terminated.
From the RE reinterview, results are also issued on a
periodic basis. They contain response variance results for
the basic labor force categories and for certain selected
other questions.
REFERENCES
Bushery, J. M., Dewey, J. A., and Weller, G. (1995),
‘‘Reinterview’s Effect on Survey Response,’’ Proceedings
of the 1995 Annual Research Conference, U.S. Census
Bureau, pp. 475-485.
U.S. Census Bureau (1996), Current Population Survey Office Manual, Form CPS-256, Washington, DC:
Government Printing Office, ch. 6.
U.S. Census Bureau (1996), Current Population Survey SFR Manual, Form CPS-251, Washington, DC: Government Printing Office, ch. 4.
U.S. Census Bureau (1993), ‘‘Falsification by Field
Representatives 1982-1992,’’ memorandum from Preston
Jay Waite to Paula Schneider, May 10, 1993.
U.S. Census Bureau (1997), ‘‘Falsification Study Results
for 1990-1997,’’ memorandum from Preston Jay Waite to
Richard L. Bitzer, May 8, 1997.
3
Beginning in February 1998, the RE reinterview respondent rule was
made the same as the QC reinterview respondent rule.
4
Prior to October 2001, RE reinterviews asked the industry and
occupation questions independently.
5
Reconciled reinterview, which was used to estimate reponse bias and
provide feedback for FR performance, was discontinued in January 1994.
H–1
Appendix H.
Sample Design Changes of the Current Population Survey:
January 1996
(See Appendix J for sample design changes in July 2001.)
INTRODUCTION
As of January 1996, the 1990 Current Population Survey
(CPS)1 sample changed because of a funding reduction.
The budget made it necessary to reduce the national
sample size from roughly 56,000 eligible housing units to
50,000 eligible housing units and from 792 sample areas to
754 sample areas. The U.S. Census Bureau and the
Bureau of Labor Statistics (BLS) decided to achieve the
budget reduction by eliminating the oversampling in CPS in
seven states and two substate areas that made it possible
to produce reliable monthly estimates of unemployment
and employment in these areas. In effect, this decision
produced the least possible damage to the precision of
national estimates with a sample reduction of this size. It
was a confirmation of the high priority attached to national
statistics and data by demographic and social subgroups
as compared to geographic detail. The four largest states
had not required any oversampling for the production of
monthly estimates, so no reductions were made in their
sample sizes. The sample was reduced in seven states:
Illinois, Massachusetts, Michigan, New Jersey, North Carolina, Ohio, and Pennsylvania; and two substate areas: Los
Angeles and New York City.
Survey Requirements
The original survey requirements stated in Chapter 3
remain unchanged except for the reliability requirements.
The new reliability requirements are:
1. The coefficient of variation (CV) on the monthly unemployment level for the Nation, given a 6 percent
unemployment rate, is 1.9 percent.
2. The CV on the annual average unemployment level
given a 6 percent unemployment rate for each of the
50 states and the District of Columbia is 8 percent or
lower.
These requirements effectively eliminated substate
area reliability requirements and monthly CV requirements for 11 large states: California, Florida, Illinois,
Massachusetts, Michigan, New Jersey, New York,
North Carolina, Ohio, Pennsylvania, and Texas. The
39 other states and the District of Columbia were not
1
The CPS produced by the redesign after the 1990 census is termed
the 1990 CPS.
affected by this sample reduction. These states kept
their original designs which meet an 8 percent CV
requirement on the annual average estimates of unemployment, assuming a 6 percent unemployment rate.
Strategy for Reducing the Sample
The sample reduction plan was designed to meet both
the national and state reliability requirements. The sample
sizes in all states and the District of Columbia, other than
the 11 largest, were already at the levels necessary to meet
the national reliability requirements, so changes in their
sizes were not required. The sample sizes in 7 of the 11
largest states and in New York City and Los Angeles were
greater than necessary to meet the new requirements, and
the sample sizes in these areas were reduced. The sample
sizes in the four most populous states — Florida, Texas,
and the parts of California and New York outside Los
Angeles and New York City — were also greater than the
precision requirements necessitated, but reductions in their
sample sizes would have created problems in achieving
the national goals. The sample sizes in these four areas
used in the redesign, therefore, were retained.
For the redesigned sample, an attempt was made to
allocate sample proportionally to the state populations in
the remaining large states and substate areas: Illinois,
Massachusetts, Michigan, New Jersey, North Carolina,
Ohio, Pennsylvania, Los Angeles, and New York City. Such
an approach would yield the same sampling rate in all of
these areas. The use of a consistent sampling rate would
tend to minimize the increase in national variance resulting
from the reduction. However, it was necessary to allow for
a fair amount of variation in the sampling rates among
these areas in order to meet the reliability requirement for
annual average data for each of the 50 states (see Table
H–2). This design results in variations in the precision
among the individual states resulting in increased expected
annual average CVs on estimates of unemployment ranging from 2.9 to 5.7 percent. The proportion of sample cut
because of the reduction and the expected CVs before and
after the reduction is shown in Table H–1. All 11 large states
have better expected reliability than the 39 other states and
the District of Columbia.
H–2
Table H–1. The Proportion of Sample Cut and Expected CVs Before and After the January 1996 Reduction
Expected CV (percent)
State
Before reduction
After reduction
Proportion of
sample cut
Monthly
Annual
Monthly
Annual
United States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.107
1.8
0.9
1.9
* 0.9
California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Texas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.024
0.059
–
–
0.130
0.478
0.320
0.348
0.195
0.348
–
0.458
0.240
0.154
0.000
6.2
9.2
7.9
8.2
7.9
7.9
7.8
8.0
6.6
9.1
8.9
8.0
7.9
8.0
7.7
2.9
4.1
3.7
3.7
3.7
3.5
3.6
3.6
3.0
4.1
4.1
3.9
3.6
3.6
3.6
6.3
9.5
7.9
8.2
8.8
11.1
9.7
9.8
7.1
11.5
8.9
11.1
9.4
8.8
7.7
2.9
4.3
3.7
3.7
4.2
4.2
4.5
4.4
3.2
5.2
4.1
5.7
4.4
4.1
3.6
* The national CV increased after the reduction but was within the rounding error.
CHANGES TO THE CPS SAMPLE DESIGN
Implementing the January 1996 sample reduction involved
modifying the original 1990 CPS state designs described in
Chapter 3. Both the first-stage and second-stage sample
designs were changed to realize major cost savings. This
differed from the maintenance reductions described in
Appendix C which affected only the second-stage design.
In addition, unlike the maintenance reductions which are
implemented over a period of 16 months, this reduction
was fully implemented in January 1996.
Many characteristics of the original design were retained.
Some of the major characteristics are:
• State-based design.
(and one group of three if there was an odd number of
strata in the state.) The super-strata were formed using a
linear programming technique to minimize the betweenstratum variation on the 1990 census levels of unemployment and civilian labor force. Then, one stratum was
selected from the super-strata with probability proportional
to the 1990 population. The sample PSU from this selected
stratum remained in sample while the sample PSU from
the nonselected stratum was dropped.
The first-stage probability of selection for the PSUs in
the new design is the product of the probability of selecting
the stratum from the super-stratum and the probability of
selecting the PSU from the stratum.
Pfs ⫽ P共stratumⱍsuper-stratum兲 x P共PSU ⱍ stratum兲
• PSU definitions.
• Self-representing PSU requirements.
• One PSU per stratum selected to be in sample.
• Systematic sample of ultimate sampling units consisting
of clusters of four housing units.
• Rotation group and reduction group assignments.
First-Stage of the Sample Design
New first-stage designs were developed by restratifying
the original 1990 design strata. First, the 1990 design
strata that were required to be self-representing (SR)
remain SR for the new designs2. For each state, any
nonrequired SR strata and all of the nonself-representing
(NSR) strata were grouped into super-strata of two strata
2
See Chapter 3 ‘‘Stratification of Primary Sampling Units’’ for the
areas which are required to be SR.
⫽
⫽
Nstratum
Nsuper-stratum
x
NPSU
Nstratum
(H.1)
NPSU
Nsuper-stratum
where
Pfs is the first-stage unconditional probability of selection of
the PSU from the super-stratum,
P(stratum|super-stratum) is the probability of selection of
the stratum from the super-stratum,
P(PSU|stratum) is the probability of selection of the PSU
from the stratum,
NPSU, Nstratum, and Nsuper-stratum are the corresponding
1990 populations.
The restratifications were developed for five states:
Illinois, Michigan, North Carolina, Ohio, and Pennsylvania.
Two states, Massachusetts and New Jersey, and two
substate areas, Los Angeles and New York City, are
entirely SR and for the most part required to be SR so no
H–3
PSUs were eliminated. The reduced CPS national design,
as of January 1996, contains 754 stratification PSUs in
sample of which 428 are SR and 326 are NSR. Table H–2
contains the number of strata by state for the nine states in
which reductions were made.
Table H–2. January 1996 Reduction: Number and Estimated Population of Strata for 754-PSU Design for the
Nine Reduced States
Self-representing (SR)
State
Strata
Estimated
population1
21
1
20
11
31
12
11
13
1
12
11
13
13
20,866,697
6,672,035
14,194,662
6,806,874
4,719,188
5,643,817
6,023,359
12,485,029
5,721,495
6,763,534
2,840,376
6,238,600
7,584,129
California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nonself-representing (NSR)
Strata
Estimated
population1
State
sampling
interval
5
0
5
6
0
5
0
7
0
7
13
6
6
1,405,500
0
1,405,500
1,821,548
0
1,345,074
0
1,414,548
0
1,414,548
2,221,874
1,958,138
1,628,780
2,745
1,779
3,132
2,227
1,853
2,098
1,841
1,970
1,774
2,093
1,986
2,343
2,149
1
Projected 1995 estimates of civilian noninstitutional population ages 16 and over.
Note: This results in an overall national sampling interval of 2,255 based on the 1995 population distribution.
Table H–3. Reduction Groups Dropped From SR
Areas
State
California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Remainder of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Number of
reduction groups
dropped
–
5
0
3
49
25
34
–
35
0
41
13
6
sampling interval required to achieve the national reliability
given the new first-stage designs. The reduction groups
were deleted in a specified order so that the nature of the
systematic sample was retained to the extent possible.
Within each state, the number of reduction groups dropped
was uniform for all SR PSUs. Table H–3 gives the number
of reduction groups dropped from each SR PSU by state.
Calculation of Sampling Intervals
The design changes do not maintain the self-weighting
nature of the original designs. The stratum sampling intervals differ for the SR and NSR portions of the sample. In
other words, the probability of selecting a housing unit
varies by stratum. The stratum sampling interval is calculated as
Second-Stage of the Sample Design
New second-stage designs were developed for SR
PSUs but not NSR PSUs. The NSR PSUs remaining in
sample were originally designed to have a manageable
workload for a single field representative. Cutting sample
housing units for NSR PSUs would make workloads inefficient for these PSUs.
Within SR PSUs, the within-PSU sampling intervals
were adjusted and subsamples of ultimate sampling units
(USU) were dropped from sample. The subsample of
USUs to drop was identified by reduction groups (see
Appendix C). The number of reduction groups to drop from
the SR PSUs in each state was a function of the state
SIstratum ⫽
SIPSU
Pfs
x
101
101 ⫺ RGD
(H.2)
where
SIstratum is the stratum sampling interval,
SIPSU is the original within-PSU sampling interval,
Pfs is the first-stage probability of selection as defined for
formula (H.1), and
RGD is the integer number of reduction groups dropped
(0 ≤ RGD ≤ 101).
Each NSR PSU has a first-stage probability of selection
calculated in formula (H.1), and the number of reduction
H–4
groups dropped equals zero. For NSR strata, formula (H.2)
reduces to
SINSR stratum ⫽
SIPSU
(H.3)
Pfs
SR strata have a first-stage probability of selection equal
to one, and the number of reduction groups dropped is
found in Table H–3. For SR strata, formula (H.2) reduces to
SISR stratum ⫽ SIPSU x
101
(H.4)
101 ⫺ RGD
The state sampling interval is a weighted average of the
stratum sampling intervals using 1990 census stratum
populations
First-Stage Ratio Adjustment Changes
The first-stage factors for CPS were recomputed in the
five states where PSUs were dropped. The first-stage ratio
adjustment factors were revised to account for the new
first-stage designs in the following states: Illinois, Michigan,
North Carolina, Ohio, and Pennsylvania. The factors did
not change for California, Massachusetts, New Jersey, and
New York. Table H–4 contains the revised CPS first-stage
factors.
Table H–4. State First-Stage Factors for CPS January
1996 Reduction
[Beginning January 1996]
H
SI ⫽
兺 Nh SIh
h⫽1
H
CPS
State
(H.5)
兺 Nh
h⫽1
where
SI is the state sampling interval,
Nh is the 1990 census population of the hth stratum in the
state,
SIh is the stratum sampling interval for the hth stratum in the
state, and
H is the total number of SR and NSR strata in the state.
Table H–2 gives the resulting state sampling intervals.
Since Massachusetts and New Jersey contain only SR
strata, they continue to have a self-weighting design.
REVISIONS TO CPS ESTIMATION
Three revisions were made to CPS estimation because
of the January 1996 sample reduction, but the general
estimation methodology was not changed. The revisions
affected only the seven states and two substate areas
affected by the reduction. The stages of weighting affected
were the basic weight, adjustment for nonresponse, and
the first-stage ratio adjustment (see Chapter 10).
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . .
Black
Non-Black
0.96670
1.00000
1.04952
0.92339
1.17540
0.99477
** 1.00000
1.00641
0.99953
0.99157
** Race cells were collapsed.
Monthly State and Substate Estimate Change
After the January 1996 sample reduction, the 11 large
states and 2 substate areas had insufficient sample to
produce reliable monthly estimates of employment and
unemployment. Now these estimates are produced using
the same ‘‘signal-plus-noise’’ time series model used for
the 39 small states and the District of Columbia discussed
in Chapter 10.
ESTIMATION OF VARIANCES
Adjustment for Nonresponse Changes
Changing the CPS design because of the January 1996
sample cut required changes to the CPS variance estimation procedures, but the variance estimation methodology
did not change. These revisions affected only the seven
states and two substate areas where the reduction occurred.
A consequence of these changes is that consistent estimates of covariances between December 1995 (and previous months) and January 1996 (and subsequent months)
cannot be produced. See Chapter 14 for a description of
the CPS variance estimation methodology.
Included in this appendix are estimates of variance
properties of the CPS design following the January 1996
reduction. These estimates replace those in Chapter 14 as
measures of the current CPS design variance properties3.
In New Jersey and North Carolina, two noninterview
clusters within each state were combined due to small
sample sizes. For January 1996 and subsequent months,
there are 125 noninterview clusters for the Nation.
3
The chapters of this document are written as of the conclusion of the
implementation of the 1990 CPS design, through December, 1995. Many
of the appendixes are written to provide updates that began in January,
1996.
Basic Weight Changes
The new basic weights for the remaining sample are the
stratum sampling intervals given in formula (H.2). These
stratum sampling intervals are the inverse of the probability
of selecting a housing unit from the stratum.
H–5
Changes to the Variance Estimation Procedure
Three changes were made to the variance estimation
procedure affecting replicate sample composition: new
standard error computation units (SECUs) were defined
(pairs of USUs), the NSR strata were combined into new
pseudostrata, and associated replicate factors were calculated using new Hadamard matrix row assignments. New
SECUs were defined in all seven states and two substate
areas because the second-stage reduction in SR PSUs
resulted in many SECUs with only one USU. NSR strata
were combined into new pseudostrata only in the five
states with new first-stage designs. These changes resulted
in a break in consistent replicate sample definitions between
December 1995 and January 1996.
CPS Variance Estimates - 754 PSU Design
Monthly levels. The estimated variance properties on
monthly levels of select labor force characteristics from the
CPS design following the January 1996 reduction are
provided in Tables H–5 to H–8. These tables replace the
corresponding Tables 14–2 to 14–5 in Chapter 14 as
indicators of the variance properties of the current CPS
design. (See previous footnote and see Chapter 14 for
description of table headings.) The variance estimates in
Tables H–5 to H–8 are averages over 12 months (January
to December 1996), while Tables 14–2 to 14–5 are averages over 6 months, making the Appendix H tables more
stable than the Chapter 14 tables.
Overall, the reduction was expected to increase CVs by
about 4 percent, based on the ratio of the square root of the
national sampling rates before and after the reduction. The
estimated CVs in Table H–5 are larger than those estimated prior to the reduction (Table 14–2) for all characteristics but Hispanic4 origin. The reliability of Hispanic origin
characteristics is also adversely affected by the reduction.
However, the variance on the Hispanic origin CVs is too
large to measure this effect.
The effect of the reduction on the contribution of the
within- and between-PSU variances to the total variance
4
Hispanics may be of any race.
(Table H–5) is even more difficult to assess because of the
variance on the variance estimates. In general, no noticeable effect on the variance estimates is found in the
contribution from the first- and second-stages of selection.
Tables H–6 and H–7 show that the sample reduction did
not appear to affect the variance properties related to
estimation. The new estimated design effects in Table H–8
appear to have increased slightly.
Month-to-month change in levels. The estimated variance properties on month-to-month change in levels of
select labor force characteristics from the CPS design
following the January 1996 reduction are in Tables H–9 and
H–10. These variance estimates are averages of 11 monthto-month change estimates.
Table H–9 indicates how the two stages of sampling
affect the variance of the composited estimates of monthto-month change. The last two columns of the table show
the percentage of the total variance due to sampling
housing units within PSUs (within-PSU variance) and the
variance due to sampling a subset of NSR PSUs (betweenPSU variance). For all characteristics in the table, the
largest component of the total variance is due to withinPSU sampling. The Hispanic origin characteristics and
teenage unemployed have smaller within-PSU components than most of the other characteristics. Total and
White employed in agriculture also have relatively small
within-PSU variance components. Negative numbers in the
last column of the table imply negative estimates of the
between-PSU variance. Because this component is relatively small and estimated by subtraction, some negative
estimates are expected. Since variances cannot be negative, the variance on the estimated variance components is
what is actually reflected in these negative estimates.
The reduction in the variance of month-to-month change
estimates due to the compositing is shown in Table H–10.
The variance factor is the variance of the composited
estimate of month-to-month change divided by the firstand second-stage combined (FSC) estimate of month-tomonth change. The factors are less than one for all but one
of the characteristics shown in the table, indicating that
compositing reduces the variance of most month-to-month
change estimates.
H–6
Table H–5. Components of Variance for FSC Monthly Estimates
[Monthly average: January - December 1996]
FSC1estimate
(x 106)
Standard error
(x 105)
Coefficient of
variation
(%)
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.31
5.34
1.61
1.14
1.32
1.48
1.26
0.71
0.62
0.59
Employed - agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.45
3.29
0.10
0.61
0.26
Employed - nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Civilian noninstitutional population, ages 16 and older
Percent of total variance
Within
Between
2.02
2.36
4.40
5.45
4.51
97.4
95.2
102.8
94.1
97.1
2.6
4.8
−2.8
5.9
2.9
1.61
1.56
0.19
0.73
0.27
4.68
4.78
19.82
12.08
11.03
70.7
69.9
95.3
91.0
104.1
29.3
30.1
4.7
9.0
−4.1
123.37
104.62
13.46
11.06
6.26
3.55
3.13
1.34
1.43
0.99
0.29
0.30
1.00
1.30
1.58
90.2
91.6
100.2
92.1
103.5
9.8
8.4
−0.2
7.9
−3.5
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
134.13
113.25
15.17
12.80
7.84
3.18
2.73
1.26
1.14
1.00
0.24
0.24
0.83
0.89
1.30
93.9
96.1
95.4
98.0
105.0
6.1
3.9
4.6
2.0
−5.0
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66.46
55.07
8.43
6.41
7.09
3.18
2.73
1.26
1.14
1.04
0.48
0.50
1.50
1.79
1.48
93.9
96.1
95.4
98.0
104.5
6.1
3.9
4.6
2.0
−4.5
1
FSC = first- and second-stage combined.
Hispanics may be of any race.
2
H–7
Table H–6. Effects of Weighting Stages on Monthly Relative Variance Factors
[Monthly averages: January - December 1996]
Relative variance factor1
Relative
variance of FSC
estimate of level
(x10-4)
Unbiased
estimator
NI2
NI & FS3
NI & SS4
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.109
5.594
19.392
30.261
19.959
1.03
1.05
1.16
1.15
1.13
1.03
1.04
1.16
1.14
1.13
1.10
1.10
1.20
1.21
1.16
1.00
1.00
0.99
1.00
1.00
Employed - agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21.763
22.695
385.214
144.816
106.648
1.00
1.00
1.09
1.05
1.06
0.98
0.98
1.08
1.03
1.05
1.10
1.09
1.06
1.20
1.07
0.93
0.93
1.02
0.95
0.99
Employed - nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.083
0.090
0.998
1.674
2.499
4.41
5.61
5.88
3.33
1.97
4.20
5.46
5.76
3.34
1.96
5.48
6.48
5.54
3.60
2.01
0.96
0.95
1.01
0.97
1.00
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.056
0.058
0.693
0.800
1.653
6.23
8.38
7.82
6.65
2.47
5.88
8.10
7.66
6.61
2.46
8.24
10.20
7.45
7.34
2.57
0.99
0.99
1.01
1.00
1.00
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
0.230
0.247
2.245
3.193
2.154
2.88
3.24
3.98
2.57
2.06
2.74
3.12
3.88
2.52
2.03
3.74
3.98
3.41
2.76
2.26
0.99
0.99
1.00
1.00
1.00
Civilian noninstitutional population, ages 16 and older
Unbiased estimator with—
1
Relative Variance Factor is the ratio of the relative variance of the specified level to the relative variance of FSC level.
NI = Noninterview
FS = First Stage
4
SS = Second Stage
5
Hispanics may be of any race.
2
3
Table H–7. Effect of Compositing on Monthly Variance and Relative Variance Factors
[Monthly averages: January - December 1996]
Civilian noninstitutional population, ages 16 and older
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed - agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed - nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Variance of composited
estimate of level
(x 109)
Variance factor1
Relative variance
factor2
21.063
15.487
4.712
3.887
3.429
23.478
22.151
0.325
4.764
0.645
109.120
85.476
15.741
18.009
8.787
86.616
64.105
14.260
11.474
9.163
86.616
64.105
14.260
11.474
9.696
0.96
0.97
0.93
0.99
0.99
0.91
0.90
0.85
0.89
0.88
0.86
0.87
0.87
0.88
0.90
0.85
0.86
0.89
0.87
0.90
0.85
0.86
0.89
0.87
0.89
0.98
0.99
0.96
1.00
1.01
0.91
0.91
0.87
0.89
0.89
0.86
0.87
0.87
0.88
0.90
0.86
0.86
0.90
0.88
0.91
0.85
0.85
0.89
0.87
0.89
Variance Factor is the ratio of the variance of the composited level to the variance of the FSC level.
Relative Variance Factor is the ratio of the relative variance of the composited level to the relative variance of the FSC level.
Hispanics may be of any race.
2
3
Note: Composite formula constants used: K = 0.4 and A = 0.2
H–8
Table H–8. Design Effects for Total Monthly Variance
[Monthly averages: January - December 1996]
Design effect
Civilian noninstitutional population, ages 16 and older
Total variance
Within-PSU variance
Not composited
Composited
Not composited
1.382
1.362
1.398
1.539
1.173
3.390
3.362
1.702
3.916
1.243
1.181
0.870
0.639
0.869
0.716
1.014
0.674
0.504
0.485
0.598
1.014
0.832
0.876
0.938
0.702
1.339
1.331
1.323
1.531
1.171
3.076
3.048
1.465
3.482
1.097
1.018
0.757
0.557
0.766
0.645
0.863
0.576
0.452
0.425
0.542
0.863
0.710
0.780
0.816
0.625
1.346
1.296
1.437
1.448
1.139
2.396
2.350
1.622
3.564
1.294
1.065
0.797
0.640
0.800
0.742
0.952
0.648
0.481
0.476
0.628
0.952
0.799
0.835
0.919
0.734
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed - agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed - nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Hispanics may be of any race.
Table H–9. Components of Variance for Composited Month-to-Month Change Estimates
[Change averages: January - December 1996]
Civilian noninstitutional population, ages 16 and older
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed - agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employed - nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Estimate of
change1
(x106)
Standard error of
change2
(x105)
0.29
0.23
0.08
0.06
0.11
0.14
0.13
0.01
0.04
0.05
0.60
0.47
0.12
0.10
0.35
0.77
0.60
0.17
0.12
0.46
0.76
0.59
0.18
0.10
0.45
1.59
1.30
0.80
0.67
0.73
0.82
0.79
0.16
0.42
0.24
2.40
2.11
1.00
0.97
0.91
2.30
2.00
0.98
0.89
0.99
2.30
2.00
0.98
0.89
1.00
The arithmetic mean of the absolute values of the 11 month-to-month changes.
The square root of the arithmetic mean of the variances of the 11 month-to-month changes.
3
The percent of the estimated total variance attributed to the two stages of sampling.
4
Hispanics may be of any race.
2
Percent of total variance3
Within
Between
98.0
102.7
100.3
96.9
93.7
92.6
92.6
103.7
88.8
101.5
102.5
105.3
100.7
94.8
100.6
106.0
106.0
98.7
97.5
101.9
106.0
106.0
98.7
97.5
100.9
2.0
−2.7
−0.3
3.1
6.3
7.4
7.4
−3.7
11.2
−1.5
−2.5
−5.3
−0.7
5.2
−0.6
−6.0
−6.0
1.3
2.5
−1.9
−6.0
−6.0
1.3
2.5
−0.9
H–9
Table H–10. Effect of Compositing on Month-to-Month Change Variance Factors
[Change averages: January - December 1996]
Variance of composited
estimate of change1
(x 109)
Variance factor2
Unemployed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25.124
16.800
6.409
4.484
5.322
0.90
0.88
0.94
0.90
0.96
Employed - agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.717
6.244
0.249
1.796
0.556
0.77
0.77
0.80
0.67
0.82
Employed - nonagriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57.684
44.718
10.007
9.397
8.228
0.70
0.68
0.76
0.70
1.02
Civilian labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52.997
39.834
9.582
7.919
9.782
0.70
0.68
0.87
0.70
0.98
Not in labor force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Teenage, 16-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52.997
39.834
9.582
7.919
9.907
0.70
0.68
0.87
0.70
0.94
Civilian noninstitutional population, ages 16 and older
1
The arithmetic mean of the 11 variance estimates.
Variance Factor is the ratio of the variance of the composited estimate to the variance of the FSC estimate.
3
Hispanics may be of any race.
2
I–1
Appendix I.
New Compositing Procedure
INTRODUCTION
COMPOSITE ESTIMATION IN THE CPS
Effective with the release of January 1998 data, BLS
implemented a new composite estimation method for the
Current Population Survey. The new technique provides
increased operational simplicity for microdata users and
allows optimization of compositing coefficients for different
labor force categories.
As described in Chapter 10, eight panels or rotation
groups, approximately equal in size, make up each monthly
CPS sample. Due to the 4-8-4 rotation pattern, six of these
panels (three-quarters of the sample) continue in sample
the following month and one-half of the households in a
given month’s sample will be back in the sample for the
same calendar month 1 year later. The sample overlap
improves estimates of change over time. Through composite estimation, the positive correlation among CPS estimators for different months is increased. This increase in
correlation improves the accuracy of monthly labor force
estimates.
The CPS AK composite estimator for a labor force total
(e.g., the number of persons unemployed) in month t is
given by
Y´ t ⫽ 共1⫺K兲Ŷt ⫹ K共Y´ t⫺1⫹䉭t兲 ⫹ A␤ˆ t
In the CPS estimation procedure, described in Chapter
10, a separate weight for each person in the CPS sample
is computed and ratio adjusted through a sequence of
weighting steps. These adjustments are followed by a
composite estimation step that improves the accuracy of
current estimates by incorporating information gathered in
previous months, taking advantage of the fact that 75
percent of sample households are common in each pair of
consecutive months. Under the old procedure, composite
estimation was performed at the macro level. The composite estimator for each tabulated cell was a function of
aggregated weights for sample persons contributing to that
cell in current and prior months. The different months of
data were combined together using compositing coefficients. Thus, microdata users needed several months of
CPS data to compute composite estimates. To ensure
consistency, the same coefficients had to be used for all
estimates. The values of the coefficients selected were
much closer to optimal for unemployment than for employment or labor force totals.
The new composite weighting method involves two
steps: (1) the computation of composite estimates for the
main labor force categories, classified by important demographic characteristics and (2) the adjustment of the microdata weights, through a series of ratio adjustments, to
agree with these composite estimates, thus incorporating
the effect of composite estimation into the microdata
weights. Under this procedure, the sum of the composite
weights of all sample persons in a particular labor force
category equals the composite estimate of the level for that
category. To produce a composite estimate for a particular
month, a data user may simply access the microdata file for
that month and compute a weighted sum. The new composite weighting approach also improves the accuracy of
labor force estimates by using different compositing coefficients for different labor force categories. The weighting
adjustment method assures additivity while allowing this
variation in compositing coefficients.
where:
8
Ŷt ⫽
䉭t ⫽
4
3
兺 xt,i
i⫽1
共xt,i ⫺ xt⫺1,i⫺1兲 and
兺
i⑀s
␤ˆ t⫽
1
xt,i
兺 xt,i⫺3 兺
i⑀s
i僆s
i
= 1,2,...,8 month in sample
xt,i
= sum of weights after second-stage ratio adjustment
of respondents in month t, and month-in-sample i
with characteristic of interest
S
= {2,3,4,6,7,8} sample continuing from previous month
K
= 0.4 for unemployed
0.7 for employed
A
= 0.3 for unemployed
0.4 for employed
The values given above for the constant coefficients A
and K are close to optimal–with respect to variance–for
month-to-month change estimates of unemployment level
and employment level. The coefficient K determines the
weight, in the weighted average, of each of two estimators
I– 2
for the current month: (1) the current month’s ratio estimator Ŷt and (2) the sum of the previous month’s composite
estimator Y´ t⫺1 and an estimator 䉭t of the change since the
previous month. The estimate of change is based on data
from sample households in the six panels common to
months t and t-1. The coefficient A determines the weight of
␤ˆ t, an adjustment term that reduces both the variance of
the composite estimator and the bias associated with time
in sample. (See Breau and Ernst 1983, Bailar 1975.)
Before January 1998, a single pair of values for K and A
was used to produce all CPS composite estimates. Optimal
values of the coefficients, however, depend on the correlation structure of the characteristic to be estimated. Research
has shown, for example, higher values of K and A result in
more reliable estimates for employment levels because the
ratio estimators for employment are more strongly correlated across time than those for unemployment. The new
composite weighting approach allows use of different compositing coefficients, thus improving the accuracy of labor
force estimates, while ensuring the additivity of estimates.
For a more detailed description of the selection of compositing parameters, see Lent et al. (1997).
direct composite estimates of UE are computed for 9
National age/sex/ethnicity cells (8 Hispanic1 age/sex
cells and 1 non-Hispanic cell) and 66 National age/sex/race
cells (38 White age/sex cells, 24 Black age/sex cells,
and 4 other age/sex cells). These computations use
cell definitions specified in the second-stage ratio
adjustment (Tables 10– 2 and 10– 3), excluding cells
containing only persons aged 15 or under. Coefficients
K=0.4 and A=0.3 are used for all UE estimates in all
categories (state, age/sex/ethnicity, and age/sex/race).
2. Sample records are classified by state. Within each
state j, a simple estimate of UE, simp(UEj), is computed by adding the weights of all unemployed sample
persons in the state.
3. Within each state j, the weight of each unemployed
sample person in the state is multiplied by the following ratio: comp(UEj) / simp(UEj).
4. Sample records are cross-classified by age, sex, and
ethnicity. Within each cross-classification cell, a simple
estimate of UE is computed by adding the weights (as
adjusted in step 3) of all unemployed sample persons
in the cell.
COMPUTING COMPOSITE WEIGHTS
5. Weights are adjusted within each age/sex/ethnicity cell
in a manner analogous to step 3.
Composite weights are produced only for sample persons age 16 or older. As described in Chapter 10, the CPS
estimation process begins with the computation of a ‘‘baseweight’’ for each adult in the survey. The baseweight– the
inverse of the probability of selection– is adjusted for nonresponse, and two successive stages of ratio adjustments
to population controls are applied. The second-stage raking procedure, performed independently for each of the
eight sample rotation groups, ensures that sample weights
add to independent population controls for states, as well
as for age/sex/ethnicity groups and age/sex/race groups,
specified at the national level.
The post-January 1998 method of computing composite
weights for the CPS imitates the second-stage ratio adjustment. Sample person weights are raked to force their sums
to equal control totals. Composite labor force estimates are
used as controls in place of independent population estimates. The composite raking process is performed separately within each of the three major labor force categories:
employed, unemployed, and those not in the labor force.
Adjustment of microdata weights to the composite estimates for each labor force category proceeds as follows.
For simplicity, we describe the method for estimating the
number of people unemployed (UE); analogous procedures are used to estimate the number of people employed
and the number not in the labor force. Data from all eight
rotation groups are combined for the purpose of computing
composite weights.
6. Steps 4 and 5 are repeated for age/sex/race cells.
1. For each state and the District of Columbia (51 cells) j,
the direct (optimal) composite estimate of UE,
comp(UEj), is computed as described above. Similarly,
7. Steps 2-6 are repeated five more times for a total of six
iterations.
An analogous procedure is done for estimating the
number of people employed using coefficients K=0.7 and
A=0.4.
For not in labor force (NILF) the same raking steps are
performed, but the controls are obtained as the residuals
from the population controls and the direct composite
estimates for employed (E) and unemployed (UE). The
formula is NILF = Population – (E + UE). During computation of composite weights for persons who are unemployed, some further collapsing of cells is needed where
cells contain insufficient sample. Any such collapsing of
cells for UE leads to a similar collapsing for NILF.
A NOTE ABOUT FINAL WEIGHTS
Prior to the introduction of composite weights the weight
after the second-stage ratio adjustment was called the final
weight, as stated in Chapter 10. This is no longer true since
the weight after compositing is now the final weight. In
addition, since data from all eight rotation groups are
combined for the purpose of computing composite weights,
summations of final weights within each panel do not
match independent population controls. However, summations of final weights for the entire sample still match these
independent population controls.
1
Hispanics may be of any race.
I– 3
REFERENCES
Breau, P., and L. Ernst, (1983). ‘‘Alternative Estimators
to the Current Composite Estimator.’’ Proceedings of the
Section on Survey Research Methods, American Statistical Association, pp. 397-402.
Bailar, B., (1975). ‘‘The Effects of Rotation Group Bias
on Estimates From Panel Surveys.’’ Journal of the American Statistical Association, 70, pp. 23-30.
Lent, J., S. Miller, P. Cantwell, and M. Duff, (1999).
‘‘Effect of Composite Weights on Some Estimates From the
Current Population Survey.’’ Journal of Official Statistics,
to appear.
J-1
Appendix J.
Changes to the Current Population Survey Sample
in July 2001
INTRODUCTION
In 1999, Congress allocated $10 million annually to the
Census Bureau to ‘‘make appropriate adjustments to the
annual Current Population Survey . . . in order to produce
statistically reliable annual state data on the number of
low-income children who do not have health insurance
coverage, so that real changes in the uninsured rates of
children can reasonably be detected.’’ (Public Law 106113)
The health insurance coverage estimates are derived
from the Annual Demographic Supplement (ADS) which is
discussed in Chapter 11. Briefly, the ADS is a series of
questions asked each March in addition to the regular CPS
questions. Consequently, the ADS is also known as the
‘‘March Supplement.’’
To achieve the objectives of the new law, the Census
Bureau added sample to the ADS in three different ways:
A new sample designation code beginning with ‘‘B’’ was
assigned to cases included in the general sample increase
to distinguish SCHIP cases from regular CPS sample
cases, which use an ‘‘A.’’ The rotation group code was not
reassigned in order to maintain the structure of the sample.
In states targeted for the general sample increase, sample
was added in all existing CPS sample areas.
The general sample increase differs from the other parts
of the SCHIP expansion in two ways:
• It affects CPS every month of the year; and
• It is implemented differently for different states. More
sample was added in same states, while other states
received no general sample increase.
For the other two parts of the SCHIP expansion, each
state’s sample size was increased proportional to the
amount of CPS sample already there.
• General sample increase in selected states
• ‘‘Split-path’’ assignments
• Month-in-sample (MIS) 9 assignments
These changes are collectively known as the State
Children’s Health Insurance Program (SCHIP) sample
expansion. The procedures used to implement the SCHIP
sample expansion were chosen in order to minimize the
effect on the basic CPS. The general sample increase will
be explained first. Later sections of this appendix provide
details on the split-path and MIS 9 assignments.
GENERAL SAMPLE INCREASE IN SELECTED
STATES
The first part of the SCHIP plan expanded basic monthly
CPS sample in selected states, using retired1 sample from
CPS. Sample was identified using CPS sample designation, rotation group codes and all four frames. Expanding
the monthly CPS was necessary, rather than simply interviewing many more cases in March, because of the
difficulty in managing a large spike in the sample size for a
single month in terms of data quality and staffing.
1
‘‘Retired sample’’ consists of households that finished their series of
eight attempted CPS interviews.
Allocating the Sample to States (Round 1)
The estimates to be improved by the SCHIP sample
expansion are 3-year averages of the percentage of lowincome children without health insurance. Low-income
children are defined, for SCHIP purposes, as those in
households with incomes at or below 200 percent of the
poverty level. To determine the states where added sample
would be of the most benefit, the detectable difference of
two consecutive 3-year averages was calculated (example:
comparing the 1998-2000 3-year average to the 19992001 3-year average). The detectable difference is the
smallest percentage difference between the two estimates
that is statistically significant at the 90-percent confidence
level.
To reassign only portions of retired CPS sample, the
natural units to use were reduction groups. The assignment
of reduction group codes is explained in Appendix C. Using
reduction group codes, the sample was divided into eight
approximately equal parts called expansion groups. This
was done to simplify the iteration process performed to
reassign sample. Each expansion group contains 12 to 13
reduction groups. In most states, only some of the eight
expansion groups were reassigned sample designations.
Reassigning all eight expansion groups is roughly equivalent to doubling the sample size, meaning that all expired
sample from the old CPS sample designations and rotation
groups is brought back to be interviewed again.
J-2
The uninsured estimates used for allocating the sample
can be found at
http://www.census.gov/hhes/hlthins/liuc98.html.
To find the current detectable difference for each state, a
variation of a formula found in the Source and Accuracy
statement (http://www.bls.census.gov/cps/ads/1998/ssrcacc.htm)
for the March Supplement was used:
se x , p ⫽
冑
bf 2
x
p 共100 ⫺ p兲
(1)
where: sex,p = standard error of the proportion
x
= the total number of children meeting the
low-income criteria
p
= the percentage of low-income children without health insurance
b
= a general variance function (GVF) parameter
= state factor to adjust for differences in the
f2
CPS sample design between states
The same proportion (p) was used for all of the states
(the 3-year national average from 1996 to 1998, 24.53
percent); this is consistent with CPS procedures to not
favor one state over another, based upon rates that may
change. Because the estimates are 3-year averages, some
manipulation was necessary to find the standard error of
the difference from one year to the next. To do so, the
following formula was used:
se共p234 ⫺ p123兲 ⫽
1
3
公se 共p4兲2 ⫹ se 共p1兲2
(2)
where p123 is the average estimate for years 1, 2, and 3,
and p123 is the average for years 2, 3, and 4. Since the
single year standard error estimates were not available, the
standard errors calculated using formula (1) were used for
both standard errors beneath the radical. The detectable
difference was calculated by finding the coefficient of
variation (by dividing by 24.53 percent), then multiplying by
1.645 to create a 90-percent confidence interval. The
resulting detectable difference is proportional to the standard errors calculated.
Of the total annual funds allocated for this project, $7
million was budgeted for the general sample increase. This
translated into approximately 12,000 additional assigned
housing units each month. The 12,000 additional housing
units were allocated among states by applying the formula
below over several iterations, with the goal of lowering
each state’s detectable difference on the health insurance
coverage rate for poor children:
SS ’ ⫽ SS *
冉 冊
DD
2
DD ’
where: SS’ = new state sample size
SS = old state sample size
DD = old detectable difference for the state
(3)
DD’ = new detectable difference for the state (The
number being decreased with each iteration.)
An increase of 12,000 housing units was reached when
the state detectable differences were lowered to a maximum of 10.01 percent. There were some states that did not
achieve this threshold, even after the sample size was
doubled. For example, Connecticut started with a detectable difference of 14.51 percent. When lowering the desired
detectable difference by iteration, the necessary sample
size there doubled when the detectable difference reached
10.25 percent. No more sample was added there because
of the decision to no more than double the existing sample
in any state. The iterative process continued to lower the
detectable difference (and raise the sample size) for the
remainder of the states, however.
Alabama started with a detectable difference of 10.81
percent. After adding one expansion group, the detectable
difference was 10.19 percent. After adding two, the detectable difference was 9.67 percent. Since the limit was 10.01
percent, only two expansion groups were added for Alabama. Arizona started with a detectable difference of 8.34
percent. Since the limit was 10.01 percent, no sample was
added there.
Round 2 of State Allocation
The proposed allocation of sample to the states was
then sent to the Census Bureau’s Field Division for approval.
Based upon their comments, the proposed sample increases
were scaled back in Maryland, Virginia, Delaware, and the
District of Columbia. After setting limits on the amount of
sample to be added to these areas, the iteration process
was continued to get back to roughly 12,000 housing units
added to sample. All states where four or more expansion
groups had already been added were also removed from
consideration. These states were prohibited from getting
further sample increases because of the proportionally
large increases they had already received. After these final
iterations, an extra expansion group was assigned in Iowa,
North Dakota, Nevada, Oklahoma, Oregon, Utah, West
Virginia, and Wyoming. These were states where three or
fewer expansion groups had been added in Round 1. Table
J-1 shows the final allocation of the general sample increase
to states.
Phase-in of the General Sample Increase
Successful implementation of the other two parts of the
SCHIP expansion required that the general sample increase
be fully implemented by November 2000. To provide enough
time for listing and keying the new sample assignments,
SCHIP interviews could not begin until September 2000
(for more details on these processes, see Chapter 4). It
was advantageous to begin adding sample as soon as
possible to make the additions as gradual as possible for
the field staff.
J-3
Table J-1. Size of SCHIP General Sample Increase and Effect on Detectable Difference by State
After first iteration
After final iteration
Original
detectable
difference
(percent)
Expansion
groups
added
Detectable
difference
(percent)
Expansion
groups
added
Detectable
difference
(percent)
Alabama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arkansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Connecticut. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
District of Columbia. . . . . . . . . . . . . . . . . . . . . . . .
Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.81
11.54
8.34
8.87
4.00
12.68
14.51
12.59
11.31
5.93
2
3
0
0
0
5
8
5
3
0
9.67
9.84
8.34
8.87
4.00
9.95
10.26
9.88
9.65
5.93
2
3
0
0
0
5
8
3
2
0
9.67
9.84
8.34
8.87
4.00
9.95
10.26
10.74
10.12
5.93
Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Indiana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kansas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kentucky . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Louisiana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.88
12.11
9.30
6.57
12.76
11.38
11.75
10.79
9.25
13.64
0
4
0
0
5
3
4
2
0
7
8.88
9.89
9.30
6.57
10.01
9.71
9.60
9.65
9.25
9.96
0
4
0
0
5
4
4
2
0
7
8.88
9.89
9.30
6.57
10.01
9.29
9.60
9.65
9.25
9.96
Maryland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mississippi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Missouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Montana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nebraska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nevada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Hampshire . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.65
9.40
7.23
11.82
8.92
11.84
9.23
11.31
11.71
14.75
8
0
0
4
0
4
0
3
3
8
10.36
9.40
7.23
9.65
8.92
9.67
9.23
9.64
9.98
10.43
5
0
0
4
0
4
0
3
4
8
11.50
9.40
7.23
9.65
8.92
9.67
9.23
9.64
9.56
10.43
New Jersey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Dakota . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oklahoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oregon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rhode Island . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.53
7.96
4.71
8.17
10.93
7.01
9.97
11.35
6.93
14.93
0
0
0
0
2
0
0
3
0
8
8.53
7.96
4.71
8.17
9.78
7.01
9.97
9.68
6.93
10.56
0
0
0
0
3
0
1
4
0
8
8.53
7.96
4.71
8.17
9.32
7.01
9.40
9.27
6.93
10.56
South Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . .
South Dakota . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tennessee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Texas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Utah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Virginia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wisconsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wyoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.19
11.28
10.01
4.78
9.78
13.40
12.02
12.28
10.62
12.59
10.44
3
3
1
0
0
7
4
5
1
5
1
9.55
9.62
9.44
4.78
9.78
9.79
9.81
9.63
10.01
9.88
9.84
3
3
1
0
1
7
2
5
2
5
2
9.55
9.62
9.44
4.78
9.22
9.79
10.75
9.63
9.50
9.88
9.33
State
J-4
Table J-2. Phase-in Table (First 11 Months of Sample Expansion)
Old sample
designation
New sample
designation
Rotation group
(both old and
new)
Last month in
sample
previously
MIS upon return
to sample
Sep. 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A63
A65
B71
B73
6
2
Aug. 1995
Aug. 1996
5
1
Oct. 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A63
A65
A65
B71
B73
B73
7
1
3
Sep. 1995
July 1996
Sep. 1996
5
3
1
Nov. 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A63
A63
A65
B71
B71
B73
5
8
4
July 1995
Oct. 1995
Oct. 1996
8
5
1
Dec. 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A65
B72
B73
1
5
Nov. 1995
Nov. 1996
5
1
Jan. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A65
B72
B73
2
6
Dec. 1995
Dec. 1996
5
1
Feb. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A65
B72
B73
3
7
Jan. 1996
Jan. 1997
5
1
Mar. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A65
B72
B73
4
8
Feb. 1996
Feb. 1997
5
1
Apr. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A66
B72
B74
5
1
Mar. 1996
Mar. 1997
5
1
May 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A66
B72
B74
6
2
Apr. 1996
Apr. 1997
5
1
Jun. 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A66
B72
B74
7
3
May 1996
May 1997
5
1
Jul. 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A64
A66
B72
B74
8
4
June 1996
June 1997
5
1
Aug. 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A66
B74
5
July 1997
1
New first month in sample
Table J-2 shows the phase-in of the expanded sample.
The sample designation/rotation groups in italics did not
enter sample MIS 1 or 5.2 B73(1) entered at MIS 3, and
B71(5) entered at MIS 8. Once introduced, these cases
followed the regular CPS rotation pattern. By November
2000, the phase-in of the general sample increase was
completed. However, because of the 4-8-4 rotating design
of the CPS sample, it was necessary to continue adding
new SCHIP cases to MIS 1 and 5 assignments for the next
8 months. In other words, for the next 8 months, SCHIP
sample was being interviewed for the first time in both MIS
1 and MIS 5, rather than just MIS 1. New SCHIP cases
were added only to MIS 1 assignments after July 2001.
A rotation chart for SCHIP follows (Figure J-1), for those
who are more familiar with this method of visualizing
survey sample. An explanation of rotation charts is found in
Chapter 3.
Use of General Sample Increase in Official
Labor Force Estimates
Although the original purpose of the SCHIP general
sample increase focused on improving the reliability of
2
To maintain the known properties of the sample rotation pattern, it is
desirable for CPS sample to begin interviewing in MIS 1. If sample cannot
be ready (for example, permit sample that does not finish processing) by
MIS 1, then it is deferred to MIS 5, thus getting four interviews in
consecutive months. However, in this case there was no choice but to add
sample at other points in the CPS interview cycle, because of the short
time frame available for phase-in.
estimates of low-income uninsured children, the Bureau of
Labor Statistics (BLS) saw the increase in the monthly CPS
sample size as an opportunity to improve the reliability of
labor force estimates, particularly at the state level. However, the added SCHIP sample initially was not used in the
official CPS labor force estimates, for a couple of reasons:
• To give Field Division the time to train the new field
representatives, and
• To analyze the labor force estimates both with and
without the new sample, ensuring that no bias was
introduced into the official time series.
In order to gauge the effect of the additional sample on
estimates, CPS data were weighted both with and without
the expanded sample beginning in November 2000. The
analysis mentioned compared the estimates as calculated
using both sets of weights. CPS weights for all SCHIP
sample are 0, while combined CPS/SCHIP weights are the
same for SCHIP sample and CPS sample cases within
each Primary Sampling Unit (PSU).
Weighting the CPS sample with the SCHIP sample included
was done using this formula:
BWCPS ⲐSCHIP ⫽ BWCPS *
(
RGCPS
)
RGCPS ⫹ RGSCHIP
(4)
J-5
Figure J-1. SCHIP Rotation Chart: September 2000 to December 2002
J-6
where: BWCPS/SCHIP = baseweight of combined CPS and
SCHIP sample
BWCPS
RGSCHIP
RGCPS
= baseweight of CPS sample alone
= reduction groups added in SCHIP
expansion (as part of appropriate
expansion groups)
= reduction groups currently in CPS
sample (usually 101)
The updated baseweights (or sampling intervals) can be
found in Table J-4, along with updated coefficients of
variation. For more details on weighting, see Chapter 10.
After analyzing several months of data, with and without
the added SCHIP sample, BLS decided to include the
SCHIP data in the official monthly CPS estimates, beginning with the July 2001 data release. The national unemployment rates were virtually identical when estimated from
the sample with and without the additional SCHIP cases.
The SCHIP expansion did not result in systematically
higher or lower labor force estimates across states, although
there were a few statistically significant differences in some
months in a few states. Results for a typical month are
shown in Table J-3.
The additional sample will slightly improve the reliability
of national labor force estimates, and will more substantially improve the state estimates for states in which SCHIP
sample was added (see U.S. Department of Labor, Employment & Earnings, August 2001, for more details). Table J-4
shows the effect of the general sample increase on state
sampling intervals and expected coefficients of variation
(CVs) for the annual average estimated levels of unemployed assuming a 6 percent unemployment rate. The CVs
will be improved (made smaller) by the additional sample in
some states. The first row of the table shows the effect on
the monthly national CV on unemployment level, which is
expected to drop from about 1.9 percent to 1.8 percent.
For purposes of comparison, the state sampling intervals and CVs are included, as estimated following a sample maintenance reduction which began in December
1999. (Details on how maintenance reductions are accomplished are found in Appendix C.) All type A rates, design
effects, estimated state populations, and civilian labor force
levels used to estimate the new CVs are the same as those
used for the maintenance reduction, so the differences in
CVs shown are resulting only from the changes in the
sampling intervals due to the SCHIP general sample
increase.
Summary of CPS/SCHIP Sample Design and
Reliability Requirements
The current sample design, introduced in July 2001,
includes about 72,000 assigned housing units from 754
sample areas (see Table J-5). Sufficient sample is allocated to maintain, at most, a 1.9 percent CV on national
monthly estimates of unemployment level, assuming a
6-percent unemployment rate. This translates into a change
of 0.2 percentage point in the unemployment rate being
significant at a 90-percent confidence level. For each of the
50 states and for the District of Columbia, the design
maintains a CV of at most 8 percent on the annual average
estimate of unemployment level, assuming a 6-percent
unemployment rate. About 60,000 assigned housing units
are required in order to meet the national and state
reliability criteria. Due to the national reliability criterion,
estimates for several large states are substantially more
reliable than the state design criterion requires. Annual
average unemployment estimates for California, Florida,
New York, and Texas, for example, carry a CV of less than
4 percent.
Table J-3. Effect of SCHIP on National Labor Force Estimates: June 2001
CPS only
CPS/SCHIP
combined
Difference
Labor force participation rate . . . . . . . . . . . . . . . . . . . . . . . . . . Total, 16 years and over
16 to 19 years
Men, 20 years and over
Women, 20 years and over
White
Black
Hispanic origin
67.4
58.1
76.5
60.5
67.6
66.2
67.9
67.4
58.5
76.6
60.4
67.7
66.1
67.8
0.0
–0.4
–0.1
0.1
–0.1
0.1
0.1
Employment-population ratio . . . . . . . . . . . . . . . . . . . . . . . . . . Total, 16 years and over
16 to 19 years
Men, 20 years and over
Women, 20 years and over
White
Black
Hispanic origin
64.2
48.5
73.6
58.0
64.8
60.4
63.4
64.2
48.6
73.7
58.0
64.9
60.5
63.4
0.0
–0.1
–0.1
0.0
–0.1
–0.1
0.0
Unemployment rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total, 16 years and over
16 to 19 years
Men, 20 years and over
Women, 20 years and over
White
Black
Hispanic origin
4.7
16.6
3.8
4.0
4.1
8.7
6.6
4.7
16.8
3.8
4.0
4.1
8.6
6.6
0.0
–0.2
0
0
0
0.1
0
Estimate
Population segment
J-7
Table J-4. Effect of SCHIP on State Sampling Intervals and Coefficients of Variation
Coefficients of variation (CVs)
of annual average level of
unemployed (percent)
Sampling intervals
State
Total . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alabama . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arkansas . . . . . . . . . . . . . . . . . . . . . . . . . . .
California . . . . . . . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . . . . . . . . . .
Balance . . . . . . . . . . . . . . . . . . . . . . . . . .
Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . .
Connecticut. . . . . . . . . . . . . . . . . . . . . . . . .
Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . .
District of Columbia. . . . . . . . . . . . . . . . . .
Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Idaho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Indiana. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kansas. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kentucky . . . . . . . . . . . . . . . . . . . . . . . . . . .
Louisiana. . . . . . . . . . . . . . . . . . . . . . . . . . .
Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maryland . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . .
Mississippi. . . . . . . . . . . . . . . . . . . . . . . . . .
Missouri . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Montana . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nebraska. . . . . . . . . . . . . . . . . . . . . . . . . . .
Nevada . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Hampshire . . . . . . . . . . . . . . . . . . . . .
New Jersey. . . . . . . . . . . . . . . . . . . . . . . . .
New Mexico . . . . . . . . . . . . . . . . . . . . . . . .
New York. . . . . . . . . . . . . . . . . . . . . . . . . . .
New York City. . . . . . . . . . . . . . . . . . . . .
Balance . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina . . . . . . . . . . . . . . . . . . . . . .
North Dakota . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oklahoma . . . . . . . . . . . . . . . . . . . . . . . . . .
Oregon. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . .
Rhode Island . . . . . . . . . . . . . . . . . . . . . . .
South Carolina . . . . . . . . . . . . . . . . . . . . . .
South Dakota . . . . . . . . . . . . . . . . . . . . . . .
Tennessee. . . . . . . . . . . . . . . . . . . . . . . . . .
Texas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Utah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Virginia. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Washington . . . . . . . . . . . . . . . . . . . . . . . . .
West Virginia . . . . . . . . . . . . . . . . . . . . . . .
Wisconsin . . . . . . . . . . . . . . . . . . . . . . . . . .
Wyoming . . . . . . . . . . . . . . . . . . . . . . . . . . .
Following 1996
reduction
(Appendix H)
Following 1999
maintenance
reduction
With SCHIP
sample included
Following 1999
maintenance
reduction
With SCHIP
sample included
2,255
2,298
336
2,238
1,316
2,745
1,779
3,132
1,992
2,307
505
356
2,176
3,077
769
590
2,227
3,132
1,582
1,423
2,089
2,143
838
3,061
1,853
2,098
2,437
1,433
3,132
431
910
961
857
1,841
867
1,970
1,774
2,093
1,986
363
2,343
1,548
1,904
2,149
687
2,291
376
3,016
2,658
958
410
3,084
2,999
896
2,638
253
2,366
2,443
336
2,238
1,316
3,018
1,919
3,438
2,163
2,307
505
356
2,338
3,451
769
608
2,227
3,132
1,582
1,452
2,153
2,186
838
3,061
1,927
2,181
2,437
1,508
3,132
463
910
1,067
892
1,898
922
2,034
1,951
2,093
2,145
363
2,376
1,629
2,046
2,149
687
2,385
376
3,140
2,796
1,063
436
3,268
3,329
896
2,638
253
2,128
1,955
244
2,238
1,316
3,018
1,919
3,438
1,331
1,154
367
285
2,338
3,451
513
608
2,227
1,927
1,055
968
1,722
2,186
447
1,884
1,927
2,181
1,625
1,508
2,088
463
662
711
446
1,898
922
2,034
1,951
2,093
2,145
264
2,376
1,448
1,364
2,149
344
1,735
273
2,791
2,796
945
232
2,619
2,048
717
1,623
202
1.9
7.2
7.4
7.0
7.2
2.9
4.3
3.7
7.0
7.7
7.4
8.0
3.7
6.3
7.8
6.8
4.2
7.0
7.2
7.3
7.2
7.0
7.6
7.1
5.0
4.5
7.3
7.3
7.1
7.2
7.2
7.0
7.5
4.4
7.2
3.2
5.2
4.3
5.6
7.4
4.4
7.4
7.3
4.1
7.8
7.4
7.1
7.0
3.6
7.1
7.3
6.8
7.6
7.1
7.4
7.3
1.8
6.5
6.5
7.0
7.2
2.9
4.3
3.7
5.8
5.4
6.3
7.1
3.7
6.3
6.4
6.8
4.2
5.7
6.2
6.2
6.5
7.0
5.7
5.7
5.0
4.5
6.3
7.3
5.9
7.2
6.3
5.8
5.3
4.4
7.2
3.2
5.2
4.3
5.6
6.5
4.4
7.1
6.1
4.1
5.5
6.4
6.3
6.6
3.6
6.8
5.4
6.2
6.3
6.4
6.3
6.7
J-8
Table J-5. CPS Sample Housing Unit Counts With and
Without SCHIP
Housing unit categories
Assigned housing units . . . . . . . . . . . . . . . . .
Eligible (occupied) . . . . . . . . . . . . . . . . . . .
Interviewed. . . . . . . . . . . . . . . . . . . . . . . .
Type A noninterviews . . . . . . . . . . . . . . .
CPS only
CPS and
SCHIP
60,000
50,000
46,800
3,200
72,000
60,000
55,500
4,500
In support of the SCHIP, about 12,000 additional housing units are allocated to the District of Columbia and 31
states. These are generally the states with the smallest
samples after the 60,000 housing units are allocated to
satisfy the national and state reliability criteria.
In the first stage of sampling, the 754 sample areas are
chosen. In the second stage, ultimate sampling unit clusters composed of about four housing units each are
selected.3 Each month, about 72,000 housing units are
assigned for data collection, of which about 60,000 are
occupied and thus eligible for interview. The remainder are
units found to be destroyed, vacant, converted to nonresidential use, to contain persons whose usual place of
residence is elsewhere, or to be ineligible for other reasons. Of the 60,000 eligible housing units, about 7.5
percent are not interviewed in a given month due to
temporary absence (such as a vacation), other failures to
make contact after repeated attempts, inability of persons
contacted to respond, unavailability for other reasons, and
refusals to cooperate (about half of the noninterviews).
Information is obtained each month for about 112,000
persons 16 years of age or older.
OTHER ASPECTS OF THE SCHIP EXPANSION
Although the general sample increase was the most
difficult to implement of the three parts of the SCHIP
expansion, the other two parts actually provide more
sample for the March Supplement. The following paragraphs provide more details on these.
supplement for that month. Only households that would
ordinarily not receive the March Supplement will receive it.
This means that February households in MIS 4 and 8 will
receive the March Supplement in February, and April MIS 1
and 5 households will receive it in April.
Households from MIS 1 and 5 in the previous November
will be screened at that time based upon whether they have
children (18 or younger) or non-White members. Those
that do meet these criteria will receive the March CPS
basic instrument and supplement in February, when they
are MIS 4 and 8, respectively. Households with Hispanic
residents will be excluded from this screening, since they
will already be interviewed as part of the November Hispanic oversampling. The March supplement will take the
place of the February supplement for these households,
effectively lowering the February supplement’s sample size.
In April, sample cases from MIS 1 and 5 will be screened
in the regular CPS interview for presence of children or
Hispanic or non-White inhabitants. Those who meet any of
these criteria will be given the March supplement instead of
the April supplement. See Chapter 11 for more details on
CPS supplements.
Month-in-Sample 9 Assignments
CPS sample from MIS 6, 7, and 8 in November will be
assigned for interview in February or April using the March
CPS basic instrument and income supplement. Before
interviewing, these households will be screened, using
CPS data, so that each household with at least one child
age 18 or younger or a non-White member will be in the
sample. (Hispanics will already be interviewed as a result
of the Hispanic oversampling of the March supplement;
see Chapter 11.) To the extent possible, these cases will be
assigned for interview through one of the telephone centers, but recycles will be interviewed in the field during the
week of the appropriate month in which CPS interviews are
conducted.
Split-Path Assignments
As part of the SCHIP expansion, some CPS sample
from February and April will receive the March CPS Supplement. This will take the place of the regularly scheduled
3
In states with the SCHIP general sample increase, the size of these
ultimate clusters can be between four and eight housing units.
EFFECT OF SCHIP EXPANSION ON STANDARD
ERRORS OF ESTIMATES OF UNINSURED
LOW-INCOME CHILDREN
Table J-6 shows the effect of the SCHIP sample expansion on the standard errors of the state-level estimates of
J-9
Table J-6. Standard Errors on Percentage of Uninsured, Low-Income Children Compared at Stages
of the SCHIP Expansion1
[In percent]
Before SCHIP
expansion
(percent)
After general
sample increase
(percent)
After SCHIP
expansion
(percent)2
Alabama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arizona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Arkansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Colorado. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Connecticut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
District of Columbia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Florida. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.61
1.72
1.24
1.32
0.60
1.89
2.16
1.88
1.69
0.88
1.44
1.47
1.24
1.32
0.60
1.48
1.53
1.60
1.51
0.88
1.22
1.30
1.14
1.17
0.53
1.32
1.34
1.41
1.29
0.80
Georgia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hawaii. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Idaho. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Indiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kansas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kentucky . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Louisiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.32
1.81
1.39
0.98
1.90
1.70
1.75
1.61
1.38
2.03
1.32
1.47
1.39
0.98
1.49
1.39
1.43
1.44
1.38
1.49
1.12
1.18
1.24
0.86
1.33
1.24
1.26
1.28
1.17
1.36
Maryland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Michigan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Missouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Montana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nebraska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nevada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Hampshire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.19
1.40
1.08
1.76
1.33
1.77
1.38
1.69
1.75
2.20
1.71
1.40
1.08
1.44
1.33
1.44
1.38
1.44
1.43
1.56
1.48
1.26
0.94
1.25
1.15
1.29
1.23
1.24
1.30
1.37
New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Mexico. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Carolina. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
North Dakota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oklahoma. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Oregon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rhode Island . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.27
1.19
0.70
1.22
1.63
1.05
1.49
1.69
1.03
2.23
1.27
1.19
0.70
1.22
1.39
1.05
1.40
1.38
1.03
1.57
1.12
1.09
0.62
1.07
1.22
0.91
1.23
1.24
0.92
1.40
South Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
South Dakota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tennessee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Texas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Utah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wisconsin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wyoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.67
1.68
1.49
0.71
1.46
2.00
1.79
1.83
1.58
1.88
1.56
1.42
1.43
1.41
0.71
1.38
1.46
1.60
1.44
1.42
1.47
1.39
1.23
1.23
1.25
0.64
1.20
1.34
1.39
1.24
1.28
1.32
1.24
State
1
Standard error on difference of two consecutive 3-year averages.
Includes MIS 9 and split-path cases.
2
J-10
the proportions of low-income children without health
insurance. These standard errors:
• Refer to the difference between two consecutive 3-year
averages,
• Are calculated after 4 years of the SCHIP expansion;
i.e. all 3 years of both estimates are expanded sample,
• Use the same rate of low-income children without
health insurance for all states.
• Are based on expected, not actual, sample sizes.
The values in the middle column (After General Sample
Increase) are proportional to the final detectable differences found in Table J-1. Included in the last column in the
table is an estimated standard error for each state after the
Split-path and MIS 9 cases are taken into account.
REFERENCES
Cahoon, L. (2000), ‘‘CPS 1990 Design Reassignment
#1 - September 2000 (SCHIP90-2, SAMADJ90-20),’’ Memorandum for J. Mackey Lewis, April 11th, Demographic
Statistical Methods Division, U.S. Census Bureau.
Rigby, B. (2000), ‘‘SCHIP CPS Sample Expansion used
to Quantify Sample Expansion Among the States (SCHIP1),’’ Memorandum for Documentation, March 29th,
Demographic Statistical Methods Division, U.S. Census
Bureau.
Shoemaker, H. (2000), ‘‘Effect of SCHIP/CPS General
Sample Expansion on State Sampling Intervals and Coefficients of Variation (SAMADJ90-22, SCHIP90-5),’’ Memorandum for Distribution List, Demographic Statistical Methods Division, U.S. Census Bureau.
U.S. Department of Labor, Bureau of Labor Statistics,
Employment and Earnings, Vol.48, No.8, Washington,
DC: Government Printing Office.
U.S. Public Law 106-113.
ACRONYMS–1
Acronyms
ADS
API
ARMA
BLS
BPC
CAPE
CAPI
CATI
CCM
CCO
CES
CESEM
CNP
CPS
CPSEP
CPSRT
CV
DA
DAFO
DEFF
DOD
DRAF
FOSDIC
FR
FSC
FSCPE
GQ
GVF
HVS
I&O
IM
INS
IRCA
LAUS
MARS
MIS
Annual Demographic Supplement
Asian and Pacific Islander
Autoregressing Moving Average
Bureau of Labor Statistics
Basic PSU Components
Committee on Adjustment of Postcensal
Estimates
Computer Assisted Personal Interviewing
Computer Assisted Telephone Interviewing
Civilian U.S. Citizens Migration
CATI and CAPI overlap
Current Employment Statistics
Current Employment Statistics Survey
Employment
Civilian Noninstitutional Population
Current Population Survey
CPS Employment-to-Population Ratio
CPS Unemployment Rate
Coefficient of Variation
Demographic Analysis
Deaths of the Armed Forces Overseas
Design Effect
Department of Defense
Deaths of the Resident Armed Forces
Film Optical Sensing Device for Input to the
Computer
Field Representative
First- and Second-Stage Combined
Federal State Cooperative Program for
Population Estimates
Group Quarters
Generalized Variance Function
Housing Vacancy Survey
Industry and Occupation
International Migration
Immigration and Naturalization Service
Immigration Reform and Control Act
Local Area Unemployment Statistics
Modified Age, Race, and Sex
Month-in-Sample
MLR
MOS
MSA
NCEUS
NCHS
NHIS
NPC
NRAF
NRO
NSR
OB
OD
OMB
ORR
PAL
PES
PIP
POP
PSU
QC
RDD
RE
RO
SCHIP
SECU
SFR
SMSA
SOC
SR
STARS
SW
TE
UE
UI
URE
USU
WPA
Major Labor Force Recode
Measure of Size
Metropolitan Statistical Area
National Commission on Employment and
Unemployment Statistics
National Center for Health Statistics
National Health Interview Survey
National Processing Center
Net Recruits to the Armed Forces
Net Recruits to the Armed Forces from
Overseas
Nonself-Representing
Number of Births
Number of Deaths
Office of Management and Budget
Office of Refugee Resettlement
Permit Address List
Post-Enumeration Survey
Performance Improvement Period
Performance Opportunity Period
Primary Sampling Unit
Quality Control
Random Digit Dialing
Response Error
Regional Office
State Children’s Health Insurance Program
Standard Error Computation Unit
Senior Field Representative
Standard Metropolitan Statistical Area
Survey of Construction
Self-Representing
State Time Series Analysis and Review
System
Start-With
Take-Every
Number of People Unemployed
Unemployment Insurance
Usual Residence Elsewhere
Ultimate Sampling Unit
Work Projects Administration
INDEX–1
Index
A
Active duty military personnel D-4
Address identification 4-1–4-4
Address lists, See Permit address lists
Adjusted population estimates definition D-1
ADS, See Annual Demographic Supplement
Age
base population by state D-18
deaths by D-15–D-16
institutional population D-18
international migration D-16–D-17
migration of Armed Forces and civilian citizens D-17
modification of census distributions D-14–D-15
population updates by D-10–D-14
Alaska
addition to population estimates 2-2
American Indian Reservations 3-9
Annual Demographic Supplement 11-2–11-8, J-1–J-10
Area frame
address identification 4-1–4-2
Area Segment Listing Sheets B-3, B-8
Area Segment Maps B-3, B-9–B-10
county maps B-9
description of 3-8–3-9
Group Quarters Listing Sheets B-3
listing in 4-5
Map Legends B-9, B-12
materials B-3, B-9
segment folders B-3
Segment Locator Maps B-9, B-11
Area Segment Maps B-3, B-9–B-10
ARMA, See Autoregressive moving average process
Armed Forces members
D-4, D-10, D-17, 11-5–11-6,
11-8
Autoregressive moving average process E-3–E-4
B
Base population
definition D-1
Baseweights (see also basic weights) I-2, 10-2
Basic PSU components 3-10–3-11
Basic weights (see also baseweights) H-4, 10-2
Behavior coding 6-3
Between-PSU variances 10-3
Births
definition D-6
distribution by sex, race, and Hispanic origin D-15
BLS, See U.S. Bureau of Labor Statistics
BPCs, See Basic PSU components
BPO, See Building permit offices
Building permit offices B-9, 3-9
Building Permit Survey 3-7, 3-9, 4-2
Building permits
permit frame 3-9–3-10
sampling 3-2
Bureau of Labor Statistics, See U.S. Bureau of Labor
Statistics
Business presence 6-4
C
California
substate areas 3-1
CAPE, See Committee on Adjustment of Postcensal
Estimates
CAPI, See Computer Assisted Personal Interviewing
CATI, See Computer Assisted Telephone Interviewing
CATI recycles 4-7, 8-2
CCM, See Civilian U.S. citizens migration
CCO, See Computer Assisted Telephone Interviewing and
Computer Assisted Personal Interviewing Overlap
Census base population D-6
Census Bureau, See U.S. Census Bureau
Census Bureau report series 12-2
Census maps B-3, B-9
Census-level population
definition D-2
CES, See Current Employment Statistics
CESEM, See Current Employment Statistics survey
employment
Check-in of sample 15-4
Civilian housing 3-8
Civilian noninstitutional population D-11, 3-1, 5-3, 10-5
Civilian population
definition D-2
migration by age, sex, race, and Hispanic origin D-17
Civilian U.S. citizens migration D-9
Class-of-worker classification 5-4
Cluster design
changes in 2-3
Clustering, geographic 4-2, 4-4
Clustering algorithm 3-4
CNP, See Census base population; Civilian noninstitutional
population
Coding data 9-1
INDEX–2
Coefficients of variation
calculating 3-1
changes in requirements H-1—H-2
changes resulting from SCHIP J-6–J-8
Collapsing cells 10-7, 10-12
College students
state population adjustment D-20
Combined Reference File listing B-3, B-9
Committee on Adjustment of Postcensal Estimates D-20
Complete nonresponse 10-2
Components of population change
definition D-2
Composite estimation
introduction of 2-2
new procedure for I-1–I-2
Composite estimators 10-9–10-10
Computer Assisted Interviewing System 2-4–2-5, 4-6
Computer Assisted Personal Interviewing
daily processing 9-1
description of 4-6–4-7, 6-2
effects on labor force characteristics 16-7–16-8
implementation of 2-5
nonsampling errors 16-3, 16-7–16-8
Computer Assisted Telephone Interviewing
daily processing 9-1
description of 4-6–4-7, 6-2, 7-6, 7-9
effects on labor force characteristics 16-7–16-8
implementation of 2-4–2-5
nonsampling errors 16-3, 16-7–16-8
training of staff F-1–F-5
transmission of 8-1–8-3
Computer Assisted Telephone Interviewing and Computer
Assisted Personal Interviewing Overlap 2-5
Computerization
electronic calculation system 2-2
questionnaire development 2-5
Conditional probabilities A-1–A-4
Consumer Income report 12-2
Control card information 5-1
Counties
combining into PSUs 3-3
selection of 3-2
County maps B-9
Coverage errors
controlling 15-2—15-4
coverage ratios 16-1—16-3
sources of 15-2
CPS, See Current Population Survey
CPS control universe
definition D-2
CPS employment-to-population ratio E-3
CPS unemployment rate E-3
CPSEP, See CPS employment-to-population ratio
CPSRT, See CPS unemployment rate
Current Employment Statistics E-1–E-2, 1-1, 10-11
Current Employment Statistics survey employment E-6
Current Population Survey
data products from 12-1–12-4
decennial and survey boundaries F-2
design overview 3-1–3-2
history of revisions 2-1–2-5
overview 1-1
participation eligibility 1-1
reference person 1-1, 2-5
requirements H-1, 3-1
sample design H-1–H-9, 3-2–3-15
sample rotation 3-15–3-16
supplemental inquiries 11-1–11-8
CV, See Coefficients of variation
D
DA population, See Demographic analysis population
DAFO, See Deaths of the Armed Forces overseas
Data collection staff
organization and training F-1–F-5
Data preparation 9-1–9-3
Data quality 13-1–13-3
Deaths
defining D-6
distribution by age D-15–D-16
distribution by sex, race, and Hispanic origin D-15
Deaths of the Armed Forces overseas D-10
Deaths of the resident Armed Forces D-10
deff, See Design effects
Demographic analysis population
definition D-2
Demographic data
for children 2-4
codes 9-2
edits 7-8, 9-2
interview information 7-7, 7-9
procedures for determining characteristics 2-4
questionnaire information 5-1–5-2
Denton Method 10-11
Department of Defense D-9, D-10
Dependent interviewing 7-5–7-6
Design effects 14-7–14-8
Direct-use states 10-11
Disabled persons
questionnaire information 6-7
Discouraged workers
definition 5-5–5-6, 6-1–6-2
questionnaire information 6-7–6-8
Document sensing procedures 2-1
DOD, See Department of Defense
Dormitory adjustment D-20
DRAF, See Deaths of the resident Armed Forces
Dual-system estimation D-20–D-22
Dwelling places 2-1
INDEX–3
E
Earnings
edits and codes 9-3
information 5-4–5-5, 6-5
weights 10-12
Edits 9-2–9-3, 15-8–15-9
Educational attainment categories 5-2
Electronic calculation system 2-2
Emigration
definition D-2
Employed persons
definition 5-3
Employment and Earnings 12-1, 14-5
The Employment Situation 12-1
Employment statistics, See Current Employment Statistics
Employment status, See also Unemployment
full-time workers 5-4
layoffs 2-2, 5-5, 6-5–6-7
monthly estimates 10-10–10-11
not in the labor force 5-5–5-6
occupational classification additions 2-3
part-time workers 2-2, 5-3–5-4
seasonal adjustment E-5, 2-2, 10-10
state model-based labor force estimation E-1–E-6
Employment-population ratio
definition 5-6
Employment-to-population ratio models 10-11
Enumerative Check Census 2-1
Estimates population
definition D-2
Estimating variance, See Variance estimation
Estimators E-1–E-6, 10-9–10-10, 13-1
Expected values 13-1
F
Families
definition 5-2
Family businesses
presence of 6-4
Family equalization 11-5
Family weights 10-12
Federal State Cooperative Program for Population
Estimates D-10, D-18
Field PSUs 3-13
Field representatives, See also Interviewers
conducting interviews 7-1, 7-3–7-6
evaluating performance F-4–F-5
performance guidelines F-3
training procedures F-1–F-2
transmitting interview results 8-1–8-3
Field subsampling 3-14–3-15, 4-6
Film Optical Sensing Device for Input to the
Computer
2-2–2-3
Final hit numbers 3-12
Final weight 10-9
First- and second-stage combined estimates 10-9, 14-5–14-7
First-stage weights 10-4, 11-4
FOSDIC, See Film Optical Sensing Device for Input to the
Computer
4-8-4 rotation system 2-1–2-2, 3-15
Friedman-Rubin clustering algorithm 3-4
FRs, See Field representatives
FSC, See First- and second-stage combined estimates
FSCPE, See Federal State Cooperative Program for
Population Estimates
Full-time workers
definition 5-4
G
Generalized variance functions C-2, 14-3–14-5
Geographic clustering 4-2, 4-4
Geographic Profile of Employment and
Unemployment 12-2
GQ, See Group quarters frame
Group Quarters frame
address identification 4-2
base population by state D-18
census maps B-9
Combined Reference File listing B-9
description of 3-7–3-9
Group Quarters Listing Sheets B-3, B-9, B-13, 4-5–4-6
Incomplete Address Locator Actions form B-9
listing in 4-5–4-6
materials B-9
segment folder B-9
GVFs, See Generalized variance functions
H
Hadamard matrix 14-2
Hawaii
addition to population estimates 2-2
Hispanic origin
distribution of births and deaths D-15
institutional population D-18
international migration D-16–D-17
migration of Armed Forces and civilian citizens
mover and nonmover households 11-5–11-8
population updates by D-10–D-11
Hit strings 3-11–3-12
Homeowner vacancy rates 11-2
Horvitz-Thompson estimators 10-9
‘‘Hot deck’’ allocations 9-2–9-3, 11-5
Hours of work
definition 5-3
questionnaire information 6-4
Households
base population by state D-18–D-19
definition 5-1–5-2
edits and codes 9-2
eligibility 7-1, 7-3
mover and nonmover households 11-5–11-8
questionnaire information 5-1–5-2
D-17
INDEX–4
Households—Con.
respondents 5-1
rosters 5-1, 7-3, 7-6
weights 10-12
Housing units
definition 3-7
frame omission 15-2
interview information 7-4–7-5, 7-9
measures 3-8
misclassification of 15-2
questionnaire information 5-1
selection of sample units 3-7
ultimate sampling units 3-2
vacancy rates 11-2–11-4
Housing Vacancy Survey 7-1, 11-2–11-4
HUs, See Housing units
HVS, See Housing Vacancy Survey
I
IM, See International migration
Immigration
definition D-2
Immigration and Naturalization Service D-2, D-7, D-16
Immigration Reform and Control Act D-8, D-16
Imputation techniques 9-2–9-3, 15-8–15-9
Incomplete Address Locator Actions forms B-3, B-7, B-9
Independent population controls, See Population controls
Industry and occupation data
coding of 2-4, 8-2, 9-1, 9-3, 15-8
edits 9-3
questionnaire information 5-4, 6-4–6-5
verification 15-8
Inflation-deflation method D-11–D-14, 2-3
Initial adjustment factors 10-7
INS, See Immigration and Naturalization Service
Institutional housing 3-8
Institutional population
changes in D-10
definition D-2
population by age, sex, race, and Hispanic origin D-18
Internal consistency of a population
definition D-2
International migration
by age, sex, race, and Hispanic origin D-16–D-17
definition D-2
population projections D-7–D-9
Internet
CPS reports 12-2
employment statistics 12-1
questionnaire revision 6-3
Interview week 7-1
Interviewers, See also Field representatives
assignments 4-6–4-7
controlling response error 15-7
debriefings 6-3
interaction with respondents 15-7–15-8
Spanish-speaking 7-6
Interviews
beginning questions 7-5, 7-7
dependent interviewing 7-5–7-6
ending questions 7-5, 7-7
household eligibility 7-1, 7-3
initial 7-3
noninterviews 7-1, 7-3, 7-4
reinterviews D-20–D-22, G-1–G-2, 15-8
results 7-10
telephone interviews 7-4–7-10
transmitting results 8-1–8-3
Introductory letters 7-1, 7-2
I&O, See Industry and occupation data
IRCA, See Immigration Reform and Control Act
Item nonresponse 9-2, 10-2, 16-5
Item response analysis 6-2–6-3
Iterative proportional fitting 10-5
J
Job leavers 5-5
Job losers 5-5
Job seekers
definition 5-5
duration of search 6-6–6-7
methods of search
6-6
Joint probabilities A-1–A-4
L
Labor force
adjustment for nonresponse 10-2–10-3
composite estimators 10-9–10-10
definition 5-6
edits and codes 9-2–9-3
effect of Type A noninterviews on classification 16-4–16-5
family weights 10-12
first-stage ratio adjustment 10-3–10-5
gross flow statistics 10-13–10-14
household weights 10-12
interview information 7-3
longitudinal weights 10-13–10-14
monthly estimates 10-10–10-11, 10-14
new composite estimation method I-1–I-2
outgoing rotation weights 10-12–10-13
questionnaire information 5-2–5-6, 6-2
ratio estimation 10-3
seasonal adjustment 10-10
second-stage ratio adjustment 10-5–10-9
state model-based estimation E-1–E-6
unbiased estimation procedure 10-1–10-2
veterans’ weights 10-13
Labor force participation rate
definition 5-6
LABSTAT 12-1
Large special exceptions
Unit/Permit Listing Sheets B-6
Layoffs 2-2, 5-5, 6-5–6-7
INDEX–5
Legal permanent residents
definition D-2
Levitan Commission 2-4, 6-1, 6-7
Listing activities 4-4–4-6
Listing checks 15-4
Listing Sheets
Area Segment B-3, B-8
Group Quarters B-3, B-9, B-13, 4-5–4-6
reviews 15-3–15-4
Unit/Permit B-1, B-3–B-6, B-9, B-14, 4-4–4-5
Longitudinal edits 9-2
Longitudinal weights 10-13–10-14
M
Maintenance reductions C-1–C-2
Major Labor Force Recode 9-2–9-3
Map Legends B-9, B-12
March Supplement (see also Annual Demographic
Supplement) 11-2–11-8
Marital status categories 5-2
MARS, See Modified age, race, and sex
Maximum Overlap procedure A-1–A-4, 3-6
Mean squared error 13-1–13-2
Measure of size 3-8
Metropolitan Statistical Areas 3-3, 10-2–10-3
Microdata files 12-1–12-2, 12-4
Military housing 3-8, 3-9
MIS, See Month-in-sample
MLR, See Major Labor Force Recode
Modeling errors 15-9
Modified age, race, and sex
application of coverage ratios D-21–D-22
definition D-2
Month-in-sample 7-1, 7-4–7-5, 11-4–11-5, 11-8, 16-7–16-9
Month-In-Sample 9 J-1, J-9–J-10
Monthly Labor Review 12-1
MOS, See Measure of size
Mover households 11-5–11-8
MSAs, See Metropolitan Statistical Areas
Multiple jobholders
definition 5-3
questionnaire information 6-2, 6-4
Multiunit structures
Unit/Permit Listing Sheets B-5
Multiunits 4-4–4-5
N
National Center for Education Statistics D-20
National Center for Health Statistics D-6
National Commission on Employment and Unemployment
Statistics 2-4, 6-1, 6-7
National Health Interview Survey 3-10
National Park blocks 3-9
National Processing Center F-1
Natural increase of a population
definition D-2
NCES, See National Center for Education Statistics
NCEUS, See National Commission on Employment and
Unemployment Statistics
NCHS, See National Center for Health Statistics
Net recruits to the Armed Forces D-10
Net recruits to the Armed Forces from overseas D-10
New entrants 5-5
New York
substate areas 3-1
NHIS, See National Health Interview Survey
Nondirect-use states 10-11
Noninstitutional housing 3-8
Noninterview
adjustments 10-3, 11-4
clusters 10-2–10-3
factors 10-3
Noninterviews 7-1, 7-3–7-4, 9-1, 16-2–16-5
Nonmover households 11-5–11-8
Nonresponse H-4, 9-2, 10-2, 15-4–15-5
Nonresponse errors
item nonresponse 16-5
mode of interview 16-6–16-7
proxy reporting 16-10
response variance 16-5–16-6
time in sample 16-7–16-10
Type A noninterviews 16-3–16-5
Nonsampling errors
controlling response error 15-6–15-8
coverage errors 15-2–15-4, 16-1
definitions 13-2–13-3
miscellaneous errors 15-8–15-9
nonresponse errors 15-4–15-5, 16-3–16-5
overview 15-1–15-2
response errors 15-6
Nonself-representing primary sampling units
3-4–3-6,
10-4, 14-1–14-3
NPC, See National Processing Center
NRAF, See Net recruits to the Armed Forces
NRO, See Net recruits to the Armed Forces from overseas
NSR PSUs, See Nonself-representing primary sampling
units
O
OB, See Overseas births
Occupational data
coding of 2-4, 8-2, 9-1
questionnaire information 5-4, 6-4–6-5
OD, See Overseas deaths
Office of Management and Budget D-2, 2-2
Office of Refugee Resettlement D-8
Old construction frames 3-8–3-9, 3-11–3-12
OMB, See Office of Management and Budget
ORR, See Office of Refugee Resettlement
Outgoing rotation weights 10-12–10-13
Overseas births D-9
Overseas deaths D-9
INDEX–6
P
PALs, See Permit Address Lists
Part-time workers
definition 5-4
economic reasons 5-3
monthly questions 2-2
noneconomic reasons 5-3–5-4
Performance Improvement Period F-5
Performance Opportunity Period F-5
Permit address lists
forms B-9, B-15, B-17, 4-6
operation 4-2, 4-4, 4-6
Permit frames
address identification 4-2, 4-4
description of 3-9–3-11
listing in 4-6
materials B-9, B-17
permit address list forms B-9, B-15, B-17
Permit Sketch Map B-16–B-17
segment folder B-9
Unit/Permit Listing Sheets B-9, B-14
Permit Sketch Maps B-16–B-17
Persons on layoff
definition 5-5, 6-5–6-7
PES, See Post-Enumeration Survey
PIP, See Performance Improvement Period
POP, See Performance Opportunity Period
Population base
definition D-1
Population Characteristics report 12-2
Population controls
adjustment for net underenumeration D-20
annual revision process D-22
dual-system estimation D-20–D-22
monthly revision process D-22
nonsampling error 15-9
population projection calculations D-4–D-20
the population universe D-3–D-4
procedural revisions D-22–D-23
sources D-23, 10-6
for states D-18–D-20
terminology D-1–D-3
Population data revisions 2-3
Population estimates
definition D-2
Population projections
calculation of D-4–D-20
definition D-2
total D-4–D-10
Population universe
for CPS controls D-3–D-4
definition D-3
Post-Enumeration Survey D-1, D-2, D-20–D-22
Post-sampling codes 3-12–3-14
pps, See Probability proportional to size
President’s Committee to Appraise Employment and
Unemployment Statistics 2-3
Primary Sampling Units
adding a county A-3
basic PSU components 3-10–3-11
changes in variance estimates H-5
definition 3-2–3-3
division of states 3-2
dropping a county A-3–A-4
dropping and adding A-4
field PSUs 3-13
increase in number of 2-1–2-4
Maximum Overlap procedure A-1–A-4, 3-6
noninterview clusters 10-2–10-3
nonself-representing 3-4–3-6, 10-4, 14-1–14-3
rules for defining 3-3
selection of 3-4–3-6
self-representing 3-3–3-5, 10-4, 14-1–14-3
stratification of 3-3–3-5
Probability proportional to size (see also Chapter 3) A-1
Probability samples 10-1–10-2
Projections, population
calculation of D-4–D-20
definition D-2
total D-4–D-10
Proxy reporting 16-10
PSUs, See Primary Sampling Units
Publications 12-1–12-4
Q
QC, See Quality control
Quality control
reinterview program G-1–G-2, 15-8
Quality measures
of statistical process 13-1–13-3
Questionnaires, See also Interviews
controlling response error 15-6
demographic information 5-1–5-2
household information 5-1–5-2
labor force information 5-2–5-6
revision of 2-1, 6-1–6-8
structure of 5-1
R
Race
category modifications 2-1, 2-3–2-4
determination of 2-4
distribution of births and deaths D-15
institutional population D-18
international migration D-16–D-17
migration of Armed Forces and civilian citizens D-17
modification of census distributions D-14–D-15
population updates by D-10–D-11
racial categories 5-2
Raking ratio 10-5, 10-6–10-8
Random digit dialing sample 6-2
Random groups 3-12–3-13
INDEX–7
Random starts 3-11
Ratio adjustments
first-stage 10-3–10-5
second-stage 10-5–10-9
Ratio estimates
revisions to 2-1–2-4
two-level, first-stage procedure 2-4
RDD, See Random digit dialing sample
RE, See Response error
Recycle Reports 8-2
Reduction groups C-1–C-2
Reduction plans C-1–C-2, H-1
Reentrants 5-5
Reference dates
definition D-2
Reference persons 1-1, 2-5, 5-1–5-2
Reference week 5-3, 6-3–6-4, 7-1
Referral codes 15-8
Refusal rates 16-2–16-4
Regional offices
operations of 4-7
organization and training of data collection staff
transmitting interview results 8-1–8-3
Regression components E-1–E-2
Reinterviews D-20–D-22, G-1–G-2, 15-8
Relational imputation 9-2
Relationship information 5-2
Relative variances 14-4–14-5
Rental vacancy rates 11-2
Replicates
definition 14-1
Replication methods 14-1–14-2
Report series 12-2
Residence cells 10-3
Resident population
definition D-2–D-3
Respondents
controlling response error 15-7
debriefings 6-3
interaction with interviewers 15-7–15-8
Response distribution analysis 6-2–6-3
Response error G-1, 6-1, 15-6–15-8
Response variance 13-2, 16-5–16-6
Retired persons
questionnaire information 6-7
ROs, See Regional offices
Rotation chart 3-15–3-16, J-4–J-5
Rotation groups 3-12, 10-1, 10-12
Rotation system 2-1–2-2, 3-2, 3-15–3-16
RS, See Random starts
S
Sample characteristics
Sample data revisions
3-1
2-3
F-1–F-5
Sample design
changes due to funding reduction H-1–H-9
controlling coverage error 15-3
county selection 3-2–3-6
definition of the PSUs 3-2–3-3
field subsampling 3-14–3-15, 4-6
old construction frames 3-8–3-9
permit frame 3-9–3-10
post-sampling code assignment 3-12–3-14
quality measures 13-1–13-3
sample housing unit selection 3-7–3-14
sample unit selection 3-10
sampling frames 3-7–3-8, 3-10
sampling procedure 3-11–3-12
sampling sources 3-7
selection of the sample PSUs 3-4–3-6
stratification of the PSUs 3-3–3-5
variance estimates 14-5–14-6
within-PSU sample selection for old construction 3-12
within-PSU sort 3-11
Sample preparation
address identification 4-1–4-4
area frame materials B-3, B-9
components of 4-1
controlling coverage error 15-3–15-4
goals of 4-1
group quarters frame materials B-9
interviewer assignments 4-6–4-7
listing activities 4-4–4-6
permit frame materials B-9, B-17
unit frame materials B-1–B-3, B-4–B-8
Sample redesign 2-5
Sample size
determination of 3-1
maintenance reductions C-1–C-2
reduction in C-1–C-2, H-1, 2-3–2-5
Sample Survey of Unemployment 2-1
Sampling
bias 13-1–13-2
error 13-1–13-2
frames 3-7–3-8
intervals H-3–H-4, 3-6, J-6–J-7
variance 13-1–13-2
SCHIP, See State Children’s Health Insurance Program
School enrollment
edits and codes 9-3
School enrollment data 2-4
Seasonal adjustment of employment E-5, 2-2, 10-10
Second jobs 5-4
Second-stage adjustment 11-4–11-8
Security
transmitting interview results 8-1
SECUs, See Standard Error Computation Units
INDEX–8
Segment folders
area frames B-3
group quarters frames B-9
permit frames B-9
unit frame B-1–B-2
Segment Locator Maps B-9, B-11
Segment number suffixes 3-13
Segment numbers 3-13
Selection method revision 2-1
Self-employed
definition 5-4
Self-representing primary sampling units 3-3–3-5, 10-4,
14-1–14-3
Self-weighting samples 10-2
Senior field representatives F-1
Sex
distribution of births and deaths D-15
institutional population D-18
international migration D-16–D-17
migration of Armed Forces and civilian citizens D-17
population updates by D-10–D-11
SFRs, See Senior field representatives
Signal components E-1
Signal-plus-noise models E-1, 10-11
Simple response variance 16-5–16-6
Simple weighted estimators 10-9
Skeleton sampling 4-2
Sketch maps B-16–B-17
SMSAs, See Standard Metropolitan Statistical Areas
SOC, See Survey of Construction
Spanish-speaking interviewers 7-6
Special Studies report 12-2
Split-path J-8
SR PSUs, See Self-representing primary sampling units
Staff
organization and training F-1–F-5
Standard Error Computation Units H-5, 14-1–14-2
Standard Metropolitan Statistical Areas 2-3
State Children’s Health Insurance Program 1-1, 2-5,
3-1, 9-1, 11-4–11-8, E-1, H-1, J-1–J-10
STARS, See State Time Series Analysis and Review
System
Start-with procedure 3-14–3-15, 4-2
State sampling intervals 3-6
State supplementary samples 2-3–2-4
State Time Series Analysis and Review System E-4
State-based design 2-4
States
model-based labor force estimation E-1–E-6
monthly employment and unemployment estimates
10-10–
10-11
population controls for D-18–D-20
Statistical Policy Division 2-2
Statistics
quality measures 13-1–13-3
Subfamilies
definition 5-2
Subsampling 3-14–3-15, 4-6
Successive difference replication 14-2–14-3
Supplemental inquiries
Annual Demographic Supplement 11-2–11-8
criteria for 11-1–11-2
Housing Vacancy Survey 11-2–11-4, 11-8
Survey, See Current Population Survey
Survey instrument, See Questionnaires
Survey of Construction B-9, 4-4
Survey week 2-2
SW, See Start-with procedure
Systematic sampling 4-2–4-3
T
Take-every procedure 3-14–3-15, 4-2
TE, See Take-every procedure
Telephone interviews 7-4–7-10, 16-6–16-8
Temporary jobs 5-5
Time series component modeling E-1–E-4
Two-stage ratio estimators 10-9
Type A noninterviews 7-1, 7-3–7-4, 10-2, 16-3–16-5
Type B noninterviews 7-1, 7-3–7-4
Type C noninterviews 7-1, 7-3–7-4
U
UI, See Unemployment insurance
Ultimate sampling units
area frames 3-8–3-9
description 3-2
hit strings 3-11–3-12
reduction groups C-1–C-2
selection of 3-7
special weighting adjustments 10-2
variance estimates 14-1–14-4
Unable to work persons
questionnaire information 6-7
Unbiased estimation procedure 10-1–10-2
Unbiased estimators 13-1
Undocumented migration
definition D-3
Unemployment
classification of 2-2
definition 5-5
duration of 5-5
early estimates of 2-1
employment search information 2-3
household relationships and 2-3
monthly estimates 10-10–10-11
questionnaire information 6-5–6-7
reason for 5-5
seasonal adjustment E-5, 2-2, 10-10
state model-based labor force estimation E-1–E-6
variance on estimate of 3-6
Unemployment insurance E-1, E-6, 10-11
Unemployment rate
definition 5-6
models 10-11
INDEX–9
Union membership 2-4
Unit frame
address identification 4-1
census maps B-3
Combined Reference File listing B-3
description of 3-8
Incomplete Address Locator Actions form B-3, B-7
listing in 4-4–4-5
materials B-1–B-8
segment folders B-1–B-2
Unit/Permit Listing Sheets B-1, B-3–B-6
Unit nonresponse 10-2, 16-3–16-5
Unit segments
Unit/Permit Listing Sheets B-4
Unit/Permit Listing Sheets
B-1, B-3–B-6, B-9, B-14,
4-4–4-5
Universe population
definition D-3
URE, See Usual residence elsewhere
U.S. Bureau of Labor Statistics E-1, 1-1, 2-2, 12-1–12-2
U.S. Census Bureau 1-1, 2-2, 2-5, 4-1, 12-2
Usual residence elsewhere 7-1
USUs, See Ultimate sampling units
V
Vacancy rates 11-2
Variance estimation
changes in H-4–H-9
Variance estimation—Con.
definitions 13-1–13-2
design effects 14-7–14-8
determining optimum survey design 14-5–14-6
generalizing 14-3–14-5
objectives of 14-1
replication methods 14-1–14-2
for state and local area estimates 14-3
successive difference replication 14-2–14-3
total variances as affected by estimation 14-6–14-7
Veterans
data for females 2-4
Veterans’ weights 10-13
W
Web sites
CPS reports 12-2
employment statistics 12-1
questionnaire revision 6-3
Weighting samples I-2, 10-2–10-3, 11-3–11-4, 15-8–15-9
Work Projects Administration 2-1
Workers, See Employment status
WPA, See Work Projects Administration
X
X-11 ARIMA program
E-5, 10-10, 10-11